diff --git "a/parse/dev/fcO9Cgn-X-R/fcO9Cgn-X-R_middle.json" "b/parse/dev/fcO9Cgn-X-R/fcO9Cgn-X-R_middle.json" new file mode 100644--- /dev/null +++ "b/parse/dev/fcO9Cgn-X-R/fcO9Cgn-X-R_middle.json" @@ -0,0 +1,39431 @@ +{ + "pdf_info": [ + { + "preproc_blocks": [ + { + "type": "title", + "bbox": [ + 120, + 97, + 492, + 137 + ], + "lines": [ + { + "bbox": [ + 120, + 97, + 493, + 119 + ], + "spans": [ + { + "bbox": [ + 120, + 97, + 493, + 119 + ], + "score": 1.0, + "content": "Capturing Failures of Large Language Models via", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 213, + 116, + 399, + 139 + ], + "spans": [ + { + "bbox": [ + 213, + 116, + 399, + 139 + ], + "score": 1.0, + "content": "Human Cognitive Biases", + "type": "text" + } + ], + "index": 1 + } + ], + "index": 0.5 + }, + { + "type": "text", + "bbox": [ + 160, + 179, + 266, + 213 + ], + "lines": [ + { + "bbox": [ + 188, + 177, + 238, + 192 + ], + "spans": [ + { + "bbox": [ + 188, + 177, + 238, + 192 + ], + "score": 1.0, + "content": "Erik Jones", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 185, + 189, + 241, + 203 + ], + "spans": [ + { + "bbox": [ + 185, + 189, + 241, + 203 + ], + "score": 1.0, + "content": "UC Berkeley", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 159, + 202, + 267, + 214 + ], + "spans": [ + { + "bbox": [ + 159, + 202, + 267, + 214 + ], + "score": 1.0, + "content": "erjones@berkeley.edu", + "type": "text" + } + ], + "index": 6 + } + ], + "index": 4 + }, + { + "type": "text", + "bbox": [ + 324, + 179, + 451, + 213 + ], + "lines": [ + { + "bbox": [ + 349, + 178, + 425, + 190 + ], + "spans": [ + { + "bbox": [ + 349, + 178, + 425, + 190 + ], + "score": 1.0, + "content": "Jacob Steinhardt", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 359, + 188, + 416, + 204 + ], + "spans": [ + { + "bbox": [ + 359, + 188, + 416, + 204 + ], + "score": 1.0, + "content": "UC Berkeley", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 322, + 201, + 452, + 213 + ], + "spans": [ + { + "bbox": [ + 322, + 201, + 452, + 213 + ], + "score": 1.0, + "content": "jsteinhardt@berkeley.edu", + "type": "text" + } + ], + "index": 7 + } + ], + "index": 5 + }, + { + "type": "title", + "bbox": [ + 283, + 240, + 328, + 254 + ], + "lines": [ + { + "bbox": [ + 281, + 240, + 330, + 255 + ], + "spans": [ + { + "bbox": [ + 281, + 240, + 330, + 255 + ], + "score": 1.0, + "content": "Abstract", + "type": "text" + } + ], + "index": 8 + } + ], + "index": 8 + }, + { + "type": "text", + "bbox": [ + 142, + 267, + 469, + 420 + ], + "lines": [ + { + "bbox": [ + 141, + 267, + 471, + 279 + ], + "spans": [ + { + "bbox": [ + 141, + 267, + 471, + 279 + ], + "score": 1.0, + "content": "Large language models generate complex, open-ended outputs: instead of out-", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 141, + 277, + 470, + 291 + ], + "spans": [ + { + "bbox": [ + 141, + 277, + 470, + 291 + ], + "score": 1.0, + "content": "putting a class label they write summaries, generate dialogue, or produce working", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 141, + 288, + 469, + 302 + ], + "spans": [ + { + "bbox": [ + 141, + 288, + 469, + 302 + ], + "score": 1.0, + "content": "code. 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Our results indicate that experimental methodology from cognitive", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 141, + 409, + 423, + 421 + ], + "spans": [ + { + "bbox": [ + 141, + 409, + 423, + 421 + ], + "score": 1.0, + "content": "science can help characterize how machine learning systems behave.1", + "type": "text" + } + ], + "index": 22 + } + ], + "index": 15.5, + "bbox_fs": [ + 140, + 267, + 471, + 421 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 441, + 191, + 455 + ], + "lines": [ + { + "bbox": [ + 105, + 441, + 192, + 457 + ], + "spans": [ + { + "bbox": [ + 105, + 441, + 192, + 457 + ], + "score": 1.0, + "content": "1 Introduction", + "type": "text" + } + ], + "index": 23 + } + ], + "index": 23 + }, + { + "type": "text", + "bbox": [ + 108, + 467, + 504, + 501 + ], + "lines": [ + { + "bbox": [ + 105, + 466, + 506, + 479 + ], + "spans": [ + { + "bbox": [ + 105, + 466, + 506, + 479 + ], + "score": 1.0, + "content": "Recent large language models have achieved new, exciting capabilities. In contrast to traditional", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 106, + 478, + 506, + 490 + ], + "spans": [ + { + "bbox": [ + 106, + 478, + 506, + 490 + ], + "score": 1.0, + "content": "classifiers, these models can generate open-ended text, enabling use cases like summarization", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 108, + 488, + 485, + 501 + ], + "spans": [ + { + "bbox": [ + 108, + 488, + 485, + 501 + ], + "score": 1.0, + "content": "[Stiennon et al., 2020], dialog [Thoppilan et al., 2022], and code generation [Chen et al., 2021]", + "type": "text" + } + ], + "index": 26 + } + ], + "index": 25, + "bbox_fs": [ + 105, + 466, + 506, + 501 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 506, + 505, + 582 + ], + "lines": [ + { + "bbox": [ + 106, + 506, + 506, + 518 + ], + "spans": [ + { + "bbox": [ + 106, + 506, + 506, + 518 + ], + "score": 1.0, + "content": "The open-ended power of these systems, however, poses new reliability challenges. We must", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 106, + 517, + 505, + 528 + ], + "spans": [ + { + "bbox": [ + 106, + 517, + 505, + 528 + ], + "score": 1.0, + "content": "understand not only when systems err, but also the kinds of errors they make, as some errors are much", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 527, + 505, + 540 + ], + "spans": [ + { + "bbox": [ + 105, + 527, + 505, + 540 + ], + "score": 1.0, + "content": "more costly than others. For example, erroneous code that does not compile is less dangerous than", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 538, + 506, + 551 + ], + "spans": [ + { + "bbox": [ + 105, + 538, + 506, + 551 + ], + "score": 1.0, + "content": "code that deletes all files in the home directory. Studying how frequently an error occurs is difficult,", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 549, + 506, + 561 + ], + "spans": [ + { + "bbox": [ + 105, + 549, + 506, + 561 + ], + "score": 1.0, + "content": "as the same error (e.g. delete all files) can appear in a wide range of syntactically diverse outputs.", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 559, + 505, + 573 + ], + "spans": [ + { + "bbox": [ + 105, + 559, + 505, + 573 + ], + "score": 1.0, + "content": "In order to better reason about how complex systems err, we need methods to test whether systems", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 106, + 571, + 495, + 584 + ], + "spans": [ + { + "bbox": [ + 106, + 571, + 495, + 584 + ], + "score": 1.0, + "content": "make the same qualitative error across different prompts, even when the generated outputs differ.", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 30, + "bbox_fs": [ + 105, + 506, + 506, + 584 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 587, + 505, + 642 + ], + "lines": [ + { + "bbox": [ + 106, + 587, + 505, + 600 + ], + "spans": [ + { + "bbox": [ + 106, + 587, + 505, + 600 + ], + "score": 1.0, + "content": "To study these reliability challenges, we primarily focus on code generation models. Such models", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 597, + 506, + 610 + ], + "spans": [ + { + "bbox": [ + 105, + 597, + 506, + 610 + ], + "score": 1.0, + "content": "complete programs from comments, descriptions of code functionality, or initial lines of code.", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 609, + 505, + 622 + ], + "spans": [ + { + "bbox": [ + 106, + 609, + 505, + 622 + ], + "score": 1.0, + "content": "Code generation is particularly amenable to study since it is objective: generated solutions are", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 619, + 505, + 632 + ], + "spans": [ + { + "bbox": [ + 105, + 619, + 505, + 632 + ], + "score": 1.0, + "content": "unambiguously correct or incorrect. Yet it is also open-ended: the set of programs a model could", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 631, + 506, + 643 + ], + "spans": [ + { + "bbox": [ + 106, + 631, + 506, + 643 + ], + "score": 1.0, + "content": "output is arbitrarily large, so the rate at which a specific program is outputted is not very descriptive.", + "type": "text" + } + ], + "index": 38 + } + ], + "index": 36, + "bbox_fs": [ + 105, + 587, + 506, + 643 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 647, + 505, + 692 + ], + "lines": [ + { + "bbox": [ + 106, + 647, + 505, + 659 + ], + "spans": [ + { + "bbox": [ + 106, + 647, + 505, + 659 + ], + "score": 1.0, + "content": "Many of the reliability challenges posed by code generation models, and open-ended systems broadly,", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 658, + 506, + 669 + ], + "spans": [ + { + "bbox": [ + 105, + 658, + 506, + 669 + ], + "score": 1.0, + "content": "also arise when studying qualitative failures in human decision making. These failures, called", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 106, + 669, + 506, + 682 + ], + "spans": [ + { + "bbox": [ + 106, + 669, + 506, + 682 + ], + "score": 1.0, + "content": "cognitive biases, are systematic ways in which humans deviate from rational judgment [Tversky", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 106, + 680, + 506, + 693 + ], + "spans": [ + { + "bbox": [ + 106, + 680, + 506, + 693 + ], + "score": 1.0, + "content": "and Kahneman, 1974]. For example, Tversky and Kahneman find that humans inadequately adjust", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 228, + 506, + 241 + ], + "spans": [ + { + "bbox": [ + 105, + 228, + 506, + 241 + ], + "score": 1.0, + "content": "estimates away from initial values, and disproportionately recall distinctive examples. To uncover", + "type": "text", + "cross_page": true + } + ], + "index": 7 + }, + { + "bbox": [ + 106, + 240, + 505, + 252 + ], + "spans": [ + { + "bbox": [ + 106, + 240, + 505, + 252 + ], + "score": 1.0, + "content": "cognitive biases, Tversky and Kahneman ask questions that are crafted to systematically reveal some", + "type": "text", + "cross_page": true + } + ], + "index": 8 + }, + { + "bbox": [ + 106, + 251, + 507, + 264 + ], + "spans": [ + { + "bbox": [ + 106, + 251, + 507, + 264 + ], + "score": 1.0, + "content": "qualitative irrationality. They uncover insights into human behavior from the diverse responses,", + "type": "text", + "cross_page": true + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 262, + 417, + 273 + ], + "spans": [ + { + "bbox": [ + 106, + 262, + 417, + 273 + ], + "score": 1.0, + "content": "without complete mechanistic insight into the minds that they aim to analyze.", + "type": "text", + "cross_page": true + } + ], + "index": 10 + } + ], + "index": 40.5, + "bbox_fs": [ + 105, + 647, + 506, + 693 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "image", + "bbox": [ + 109, + 72, + 503, + 155 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 109, + 72, + 503, + 155 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 109, + 72, + 503, + 155 + ], + "spans": [ + { + "bbox": [ + 109, + 72, + 503, + 155 + ], + "score": 0.953, + "type": "image", + "image_path": "b810734399a6a31fa69f1d979a6e552103d7ab86daa2ba2d29853274d717cdaf.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 109, + 72, + 503, + 99.66666666666667 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 109, + 99.66666666666667, + 503, + 127.33333333333334 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 109, + 127.33333333333334, + 503, + 155.0 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 160, + 505, + 204 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 160, + 506, + 172 + ], + "spans": [ + { + "bbox": [ + 106, + 160, + 506, + 172 + ], + "score": 1.0, + "content": "Figure 1: Illustration of our experimental framework. We use a cognitive bias (framing effect)", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 172, + 505, + 183 + ], + "spans": [ + { + "bbox": [ + 105, + 172, + 505, + 183 + ], + "score": 1.0, + "content": "to inspire a potential code generation failure mode (relying on irrelevant information). We then", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 183, + 505, + 194 + ], + "spans": [ + { + "bbox": [ + 106, + 183, + 505, + 194 + ], + "score": 1.0, + "content": "transform inputs in a way that we suspect will elicit the failure mode (prepending sum). We evaluate", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 193, + 505, + 205 + ], + "spans": [ + { + "bbox": [ + 105, + 193, + 505, + 205 + ], + "score": 1.0, + "content": "whether the modifications lower accuracy, and if the output is an instance of the targeted failure mode.", + "type": "text" + } + ], + "index": 6 + } + ], + "index": 4.5 + } + ], + "index": 2.75 + }, + { + "type": "text", + "bbox": [ + 107, + 228, + 505, + 273 + ], + "lines": [ + { + "bbox": [ + 105, + 228, + 506, + 241 + ], + "spans": [ + { + "bbox": [ + 105, + 228, + 506, + 241 + ], + "score": 1.0, + "content": "estimates away from initial values, and disproportionately recall distinctive examples. To uncover", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 106, + 240, + 505, + 252 + ], + "spans": [ + { + "bbox": [ + 106, + 240, + 505, + 252 + ], + "score": 1.0, + "content": "cognitive biases, Tversky and Kahneman ask questions that are crafted to systematically reveal some", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 106, + 251, + 507, + 264 + ], + "spans": [ + { + "bbox": [ + 106, + 251, + 507, + 264 + ], + "score": 1.0, + "content": "qualitative irrationality. They uncover insights into human behavior from the diverse responses,", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 262, + 417, + 273 + ], + "spans": [ + { + "bbox": [ + 106, + 262, + 417, + 273 + ], + "score": 1.0, + "content": "without complete mechanistic insight into the minds that they aim to analyze.", + "type": "text" + } + ], + "index": 10 + } + ], + "index": 8.5 + }, + { + "type": "text", + "bbox": [ + 106, + 277, + 505, + 355 + ], + "lines": [ + { + "bbox": [ + 106, + 277, + 506, + 290 + ], + "spans": [ + { + "bbox": [ + 106, + 277, + 506, + 290 + ], + "score": 1.0, + "content": "In this work, we extend Tversky and Kahneman’s experimental methodology and results to elicit", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 288, + 505, + 302 + ], + "spans": [ + { + "bbox": [ + 105, + 288, + 505, + 302 + ], + "score": 1.0, + "content": "failure modes of large code and language models, without relying on complete mechanistic insight", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 300, + 505, + 313 + ], + "spans": [ + { + "bbox": [ + 106, + 300, + 505, + 313 + ], + "score": 1.0, + "content": "into their behavior (Figure 1). Given a potential failure mode (e.g. relying on irrelevant information", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 310, + 506, + 323 + ], + "spans": [ + { + "bbox": [ + 105, + 310, + 506, + 323 + ], + "score": 1.0, + "content": "in the input), we construct a transformation over inputs that largely preserves semantics, but that we", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 321, + 506, + 333 + ], + "spans": [ + { + "bbox": [ + 105, + 321, + 506, + 333 + ], + "score": 1.0, + "content": "suspect will elicit the failure (e.g. prepending an irrelevant function). We first test if the model is", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 333, + 505, + 344 + ], + "spans": [ + { + "bbox": [ + 105, + 333, + 505, + 344 + ], + "score": 1.0, + "content": "sensitive to the transformation, by measuring if it decreases accuracy. Then, we check that the model", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 106, + 344, + 504, + 356 + ], + "spans": [ + { + "bbox": [ + 106, + 344, + 504, + 356 + ], + "score": 1.0, + "content": "outputs have elements that are indicative of the targeted failure (e.g. copies the irrelevant function).", + "type": "text" + } + ], + "index": 17 + } + ], + "index": 14 + }, + { + "type": "text", + "bbox": [ + 106, + 359, + 506, + 437 + ], + "lines": [ + { + "bbox": [ + 106, + 360, + 506, + 372 + ], + "spans": [ + { + "bbox": [ + 106, + 360, + 506, + 372 + ], + "score": 1.0, + "content": "We draw on four different cognitive biases to hypothesize potential failures of OpenAI’s Codex [Chen", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 370, + 507, + 384 + ], + "spans": [ + { + "bbox": [ + 105, + 370, + 130, + 384 + ], + "score": 1.0, + "content": "et al.,", + "type": "text" + }, + { + "bbox": [ + 130, + 370, + 156, + 383 + ], + "score": 0.47, + "content": "\\boxed { 2 0 2 1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 157, + 370, + 268, + 384 + ], + "score": 1.0, + "content": "and Salesforce’s CodeGen", + "type": "text" + }, + { + "bbox": [ + 269, + 370, + 360, + 383 + ], + "score": 0.47, + "content": "[ [ \\mathrm { N i j k a m p ~ e t ~ a l . } ] [ \\overline { { 2 0 2 2 } } ] ]", + "type": "inline_equation" + }, + { + "bbox": [ + 360, + 370, + 507, + 384 + ], + "score": 1.0, + "content": ", then apply our framework to each.", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 381, + 506, + 394 + ], + "spans": [ + { + "bbox": [ + 105, + 381, + 506, + 394 + ], + "score": 1.0, + "content": "Our results indicate that these models often rely on irrelevant information when generating solutions,", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 391, + 506, + 405 + ], + "spans": [ + { + "bbox": [ + 105, + 391, + 506, + 405 + ], + "score": 1.0, + "content": "adjust solutions towards related-but-incorrect solutions, are biased based on training-set frequencies,", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 106, + 403, + 505, + 415 + ], + "spans": [ + { + "bbox": [ + 106, + 403, + 505, + 415 + ], + "score": 1.0, + "content": "and reverts to computationally simpler problems when faced with a complex calculation. We also", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 415, + 505, + 426 + ], + "spans": [ + { + "bbox": [ + 106, + 415, + 505, + 426 + ], + "score": 1.0, + "content": "apply our framework to OpenAI’s GPT-3 [Brown et al., 2020], and show that it updates its predictions", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 424, + 449, + 439 + ], + "spans": [ + { + "bbox": [ + 105, + 424, + 449, + 439 + ], + "score": 1.0, + "content": "towards anchors, and predictably adjusts its responses based on the question framing.", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 21 + }, + { + "type": "text", + "bbox": [ + 107, + 442, + 505, + 486 + ], + "lines": [ + { + "bbox": [ + 105, + 442, + 506, + 455 + ], + "spans": [ + { + "bbox": [ + 105, + 442, + 506, + 455 + ], + "score": 1.0, + "content": "Finally, we show that our framework can uncover high-impact errors: errors that are harmful and", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 452, + 506, + 465 + ], + "spans": [ + { + "bbox": [ + 105, + 452, + 506, + 465 + ], + "score": 1.0, + "content": "difficult to undo. Specifically, we use our framework to systematically generate prompts where Codex", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 463, + 505, + 477 + ], + "spans": [ + { + "bbox": [ + 105, + 463, + 505, + 477 + ], + "score": 1.0, + "content": "erroneously deletes files. Our results indicate that experimental methodology from cognitive science", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 474, + 389, + 488 + ], + "spans": [ + { + "bbox": [ + 105, + 474, + 389, + 488 + ], + "score": 1.0, + "content": "can help uncover failure modes of complex machine learning systems.", + "type": "text" + } + ], + "index": 28 + } + ], + "index": 26.5 + }, + { + "type": "title", + "bbox": [ + 107, + 504, + 197, + 517 + ], + "lines": [ + { + "bbox": [ + 105, + 503, + 198, + 519 + ], + "spans": [ + { + "bbox": [ + 105, + 503, + 198, + 519 + ], + "score": 1.0, + "content": "2 Related Work", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 29 + }, + { + "type": "text", + "bbox": [ + 106, + 531, + 506, + 609 + ], + "lines": [ + { + "bbox": [ + 106, + 530, + 506, + 542 + ], + "spans": [ + { + "bbox": [ + 106, + 530, + 506, + 542 + ], + "score": 1.0, + "content": "Large language models. Recent work has developed large, capable, autoregressive language models,", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 541, + 504, + 554 + ], + "spans": [ + { + "bbox": [ + 105, + 541, + 504, + 554 + ], + "score": 1.0, + "content": "which predict future tokens from past tokens [Radford et al., 2019, Wang and Komatsuzaki, 2021,", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 552, + 505, + 565 + ], + "spans": [ + { + "bbox": [ + 106, + 552, + 505, + 565 + ], + "score": 1.0, + "content": "Brown et al., 2020, Chen et al., 2021, Rae et al., 2021]. These models can be used for open-ended", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 104, + 563, + 505, + 576 + ], + "spans": [ + { + "bbox": [ + 104, + 563, + 505, + 576 + ], + "score": 1.0, + "content": "generation tasks such as summarization [Stiennon et al., 2020, Ziegler et al., 2019, Rothe et al., 2020],", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 573, + 504, + 587 + ], + "spans": [ + { + "bbox": [ + 105, + 573, + 504, + 587 + ], + "score": 1.0, + "content": "dialogue [Ram et al., 2018, Thoppilan et al., 2022], and long form question answering [Fan et al.,", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 584, + 506, + 598 + ], + "spans": [ + { + "bbox": [ + 105, + 584, + 131, + 598 + ], + "score": 1.0, + "content": "2019]", + "type": "text" + }, + { + "bbox": [ + 132, + 585, + 506, + 597 + ], + "score": 1.0, + "content": ", among others. Model-generated code has been used to solve both programming and statistics", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 596, + 294, + 609 + ], + "spans": [ + { + "bbox": [ + 105, + 596, + 147, + 608 + ], + "score": 1.0, + "content": "questions", + "type": "text" + }, + { + "bbox": [ + 148, + 596, + 294, + 609 + ], + "score": 1.0, + "content": "[Chen et al., 2021, Tang et al., 2021].", + "type": "text" + } + ], + "index": 36 + } + ], + "index": 33 + }, + { + "type": "text", + "bbox": [ + 106, + 612, + 505, + 723 + ], + "lines": [ + { + "bbox": [ + 106, + 611, + 506, + 624 + ], + "spans": [ + { + "bbox": [ + 106, + 611, + 506, + 624 + ], + "score": 1.0, + "content": "There is some existing work studying failures of large language models. 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Some work adversarially prompts models to leak", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 710, + 505, + 723 + ], + "spans": [ + { + "bbox": [ + 106, + 710, + 505, + 723 + ], + "score": 1.0, + "content": "training data [Carlini et al., 2020], or output specific content [Wallace et al., 2019, Carlini et al.,", + "type": "text" + } + ], + "index": 46 + } + ], + "index": 41.5 + } + ], + "page_idx": 1, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 302, + 741, + 309, + 750 + ], + "lines": [ + { + "bbox": [ + 301, + 740, + 310, + 753 + ], + "spans": [ + { + "bbox": [ + 301, + 740, + 310, + 753 + ], + "score": 1.0, + "content": "2", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "image", + "bbox": [ + 109, + 72, + 503, + 155 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 109, + 72, + 503, + 155 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 109, + 72, + 503, + 155 + ], + "spans": [ + { + "bbox": [ + 109, + 72, + 503, + 155 + ], + "score": 0.953, + "type": "image", + "image_path": "b810734399a6a31fa69f1d979a6e552103d7ab86daa2ba2d29853274d717cdaf.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 109, + 72, + 503, + 99.66666666666667 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 109, + 99.66666666666667, + 503, + 127.33333333333334 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 109, + 127.33333333333334, + 503, + 155.0 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 160, + 505, + 204 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 160, + 506, + 172 + ], + "spans": [ + { + "bbox": [ + 106, + 160, + 506, + 172 + ], + "score": 1.0, + "content": "Figure 1: Illustration of our experimental framework. We use a cognitive bias (framing effect)", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 172, + 505, + 183 + ], + "spans": [ + { + "bbox": [ + 105, + 172, + 505, + 183 + ], + "score": 1.0, + "content": "to inspire a potential code generation failure mode (relying on irrelevant information). We then", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 183, + 505, + 194 + ], + "spans": [ + { + "bbox": [ + 106, + 183, + 505, + 194 + ], + "score": 1.0, + "content": "transform inputs in a way that we suspect will elicit the failure mode (prepending sum). We evaluate", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 193, + 505, + 205 + ], + "spans": [ + { + "bbox": [ + 105, + 193, + 505, + 205 + ], + "score": 1.0, + "content": "whether the modifications lower accuracy, and if the output is an instance of the targeted failure mode.", + "type": "text" + } + ], + "index": 6 + } + ], + "index": 4.5 + } + ], + "index": 2.75 + }, + { + "type": "text", + "bbox": [ + 107, + 228, + 505, + 273 + ], + "lines": [], + "index": 8.5, + "bbox_fs": [ + 105, + 228, + 507, + 273 + ], + "lines_deleted": true + }, + { + "type": "text", + "bbox": [ + 106, + 277, + 505, + 355 + ], + "lines": [ + { + "bbox": [ + 106, + 277, + 506, + 290 + ], + "spans": [ + { + "bbox": [ + 106, + 277, + 506, + 290 + ], + "score": 1.0, + "content": "In this work, we extend Tversky and Kahneman’s experimental methodology and results to elicit", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 288, + 505, + 302 + ], + "spans": [ + { + "bbox": [ + 105, + 288, + 505, + 302 + ], + "score": 1.0, + "content": "failure modes of large code and language models, without relying on complete mechanistic insight", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 300, + 505, + 313 + ], + "spans": [ + { + "bbox": [ + 106, + 300, + 505, + 313 + ], + "score": 1.0, + "content": "into their behavior (Figure 1). Given a potential failure mode (e.g. relying on irrelevant information", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 310, + 506, + 323 + ], + "spans": [ + { + "bbox": [ + 105, + 310, + 506, + 323 + ], + "score": 1.0, + "content": "in the input), we construct a transformation over inputs that largely preserves semantics, but that we", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 321, + 506, + 333 + ], + "spans": [ + { + "bbox": [ + 105, + 321, + 506, + 333 + ], + "score": 1.0, + "content": "suspect will elicit the failure (e.g. prepending an irrelevant function). We first test if the model is", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 333, + 505, + 344 + ], + "spans": [ + { + "bbox": [ + 105, + 333, + 505, + 344 + ], + "score": 1.0, + "content": "sensitive to the transformation, by measuring if it decreases accuracy. Then, we check that the model", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 106, + 344, + 504, + 356 + ], + "spans": [ + { + "bbox": [ + 106, + 344, + 504, + 356 + ], + "score": 1.0, + "content": "outputs have elements that are indicative of the targeted failure (e.g. copies the irrelevant function).", + "type": "text" + } + ], + "index": 17 + } + ], + "index": 14, + "bbox_fs": [ + 105, + 277, + 506, + 356 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 359, + 506, + 437 + ], + "lines": [ + { + "bbox": [ + 106, + 360, + 506, + 372 + ], + "spans": [ + { + "bbox": [ + 106, + 360, + 506, + 372 + ], + "score": 1.0, + "content": "We draw on four different cognitive biases to hypothesize potential failures of OpenAI’s Codex [Chen", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 370, + 507, + 384 + ], + "spans": [ + { + "bbox": [ + 105, + 370, + 130, + 384 + ], + "score": 1.0, + "content": "et al.,", + "type": "text" + }, + { + "bbox": [ + 130, + 370, + 156, + 383 + ], + "score": 0.47, + "content": "\\boxed { 2 0 2 1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 157, + 370, + 268, + 384 + ], + "score": 1.0, + "content": "and Salesforce’s CodeGen", + "type": "text" + }, + { + "bbox": [ + 269, + 370, + 360, + 383 + ], + "score": 0.47, + "content": "[ [ \\mathrm { N i j k a m p ~ e t ~ a l . } ] [ \\overline { { 2 0 2 2 } } ] ]", + "type": "inline_equation" + }, + { + "bbox": [ + 360, + 370, + 507, + 384 + ], + "score": 1.0, + "content": ", then apply our framework to each.", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 381, + 506, + 394 + ], + "spans": [ + { + "bbox": [ + 105, + 381, + 506, + 394 + ], + "score": 1.0, + "content": "Our results indicate that these models often rely on irrelevant information when generating solutions,", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 391, + 506, + 405 + ], + "spans": [ + { + "bbox": [ + 105, + 391, + 506, + 405 + ], + "score": 1.0, + "content": "adjust solutions towards related-but-incorrect solutions, are biased based on training-set frequencies,", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 106, + 403, + 505, + 415 + ], + "spans": [ + { + "bbox": [ + 106, + 403, + 505, + 415 + ], + "score": 1.0, + "content": "and reverts to computationally simpler problems when faced with a complex calculation. We also", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 415, + 505, + 426 + ], + "spans": [ + { + "bbox": [ + 106, + 415, + 505, + 426 + ], + "score": 1.0, + "content": "apply our framework to OpenAI’s GPT-3 [Brown et al., 2020], and show that it updates its predictions", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 424, + 449, + 439 + ], + "spans": [ + { + "bbox": [ + 105, + 424, + 449, + 439 + ], + "score": 1.0, + "content": "towards anchors, and predictably adjusts its responses based on the question framing.", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 21, + "bbox_fs": [ + 105, + 360, + 507, + 439 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 442, + 505, + 486 + ], + "lines": [ + { + "bbox": [ + 105, + 442, + 506, + 455 + ], + "spans": [ + { + "bbox": [ + 105, + 442, + 506, + 455 + ], + "score": 1.0, + "content": "Finally, we show that our framework can uncover high-impact errors: errors that are harmful and", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 452, + 506, + 465 + ], + "spans": [ + { + "bbox": [ + 105, + 452, + 506, + 465 + ], + "score": 1.0, + "content": "difficult to undo. Specifically, we use our framework to systematically generate prompts where Codex", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 463, + 505, + 477 + ], + "spans": [ + { + "bbox": [ + 105, + 463, + 505, + 477 + ], + "score": 1.0, + "content": "erroneously deletes files. Our results indicate that experimental methodology from cognitive science", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 474, + 389, + 488 + ], + "spans": [ + { + "bbox": [ + 105, + 474, + 389, + 488 + ], + "score": 1.0, + "content": "can help uncover failure modes of complex machine learning systems.", + "type": "text" + } + ], + "index": 28 + } + ], + "index": 26.5, + "bbox_fs": [ + 105, + 442, + 506, + 488 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 504, + 197, + 517 + ], + "lines": [ + { + "bbox": [ + 105, + 503, + 198, + 519 + ], + "spans": [ + { + "bbox": [ + 105, + 503, + 198, + 519 + ], + "score": 1.0, + "content": "2 Related Work", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 29 + }, + { + "type": "text", + "bbox": [ + 106, + 531, + 506, + 609 + ], + "lines": [ + { + "bbox": [ + 106, + 530, + 506, + 542 + ], + "spans": [ + { + "bbox": [ + 106, + 530, + 506, + 542 + ], + "score": 1.0, + "content": "Large language models. Recent work has developed large, capable, autoregressive language models,", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 541, + 504, + 554 + ], + "spans": [ + { + "bbox": [ + 105, + 541, + 504, + 554 + ], + "score": 1.0, + "content": "which predict future tokens from past tokens [Radford et al., 2019, Wang and Komatsuzaki, 2021,", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 552, + 505, + 565 + ], + "spans": [ + { + "bbox": [ + 106, + 552, + 505, + 565 + ], + "score": 1.0, + "content": "Brown et al., 2020, Chen et al., 2021, Rae et al., 2021]. These models can be used for open-ended", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 104, + 563, + 505, + 576 + ], + "spans": [ + { + "bbox": [ + 104, + 563, + 505, + 576 + ], + "score": 1.0, + "content": "generation tasks such as summarization [Stiennon et al., 2020, Ziegler et al., 2019, Rothe et al., 2020],", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 573, + 504, + 587 + ], + "spans": [ + { + "bbox": [ + 105, + 573, + 504, + 587 + ], + "score": 1.0, + "content": "dialogue [Ram et al., 2018, Thoppilan et al., 2022], and long form question answering [Fan et al.,", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 584, + 506, + 598 + ], + "spans": [ + { + "bbox": [ + 105, + 584, + 131, + 598 + ], + "score": 1.0, + "content": "2019]", + "type": "text" + }, + { + "bbox": [ + 132, + 585, + 506, + 597 + ], + "score": 1.0, + "content": ", among others. 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Benchmarks that measure", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 622, + 504, + 636 + ], + "spans": [ + { + "bbox": [ + 105, + 622, + 504, + 636 + ], + "score": 1.0, + "content": "model performance on multiple choice questions [Wang et al., 2019b,a, Hendrycks et al., 2021b],", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 634, + 505, + 647 + ], + "spans": [ + { + "bbox": [ + 105, + 634, + 505, + 647 + ], + "score": 1.0, + "content": "mathematics [Hendrycks et al., 2021c, Cobbe et al., 2021], long-form question answering [Lin et al.,", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 644, + 506, + 659 + ], + "spans": [ + { + "bbox": [ + 105, + 644, + 506, + 659 + ], + "score": 1.0, + "content": "2021, Gabriel et al., 2021, Shuster et al., 2021, Krishna et al., 2021], and coding problems [Hendrycks", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 654, + 507, + 669 + ], + "spans": [ + { + "bbox": [ + 105, + 656, + 128, + 667 + ], + "score": 1.0, + "content": "et al.,", + "type": "text" + }, + { + "bbox": [ + 129, + 654, + 507, + 669 + ], + "score": 1.0, + "content": "2021a, Chen et al., 2021] reveal inputs that the model errs on, but not the kind of error it makes.", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 666, + 506, + 679 + ], + "spans": [ + { + "bbox": [ + 105, + 666, + 506, + 679 + ], + "score": 1.0, + "content": "Another line of work shows that test-based language models can internalize bias and stereotypes", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 108, + 677, + 506, + 690 + ], + "spans": [ + { + "bbox": [ + 108, + 677, + 506, + 690 + ], + "score": 1.0, + "content": "[Sheng et al., 2019, Nadeem et al., 2020, Groenwold et al., 2020, Blodgett et al., 2021, Gehman", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 689, + 505, + 702 + ], + "spans": [ + { + "bbox": [ + 105, + 689, + 505, + 702 + ], + "score": 1.0, + "content": "et al., 2020], and proposes applying fairness measurements from cognitive social sciences to machine", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 700, + 505, + 711 + ], + "spans": [ + { + "bbox": [ + 106, + 700, + 505, + 711 + ], + "score": 1.0, + "content": "learning systems [Jacobs and Wallach, 2021]. 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Our framework builds on this work by", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 412, + 505, + 426 + ], + "spans": [ + { + "bbox": [ + 105, + 412, + 505, + 426 + ], + "score": 1.0, + "content": "(i) identifying the link to cognitive biases, (ii) focusing on open-ended generation, and (iii) leveraging", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 424, + 456, + 436 + ], + "spans": [ + { + "bbox": [ + 106, + 424, + 456, + 436 + ], + "score": 1.0, + "content": "Tversky and Kahneman’s experimental methodology to elicit qualitative failure modes.", + "type": "text" + } + ], + "index": 23 + } + ], + "index": 20.5 + }, + { + "type": "title", + "bbox": [ + 108, + 450, + 282, + 464 + ], + "lines": [ + { + "bbox": [ + 104, + 448, + 283, + 468 + ], + "spans": [ + { + "bbox": [ + 104, + 448, + 283, + 468 + ], + "score": 1.0, + "content": "3 Code Generation Experiments", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 24 + }, + { + "type": "title", + "bbox": [ + 106, + 474, + 162, + 486 + ], + "lines": [ + { + "bbox": [ + 105, + 474, + 162, + 487 + ], + "spans": [ + { + "bbox": [ + 105, + 474, + 162, + 487 + ], + "score": 1.0, + "content": "3.1 Models", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 25 + }, + { + "type": "text", + "bbox": [ + 106, + 494, + 505, + 539 + ], + "lines": [ + { + "bbox": [ + 105, + 494, + 507, + 508 + ], + "spans": [ + { + "bbox": [ + 105, + 494, + 507, + 508 + ], + "score": 1.0, + "content": "We study two code models: OpenAI’s Codex [Chen et al., 2021], and Salesforce’s CodeGen. 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Zhao et al.", + "type": "text" + }, + { + "bbox": [ + 478, + 370, + 504, + 382 + ], + "score": 1.0, + "content": "[2021]", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 379, + 505, + 393 + ], + "spans": [ + { + "bbox": [ + 105, + 381, + 163, + 392 + ], + "score": 1.0, + "content": "and Liu et al.", + "type": "text" + }, + { + "bbox": [ + 164, + 379, + 505, + 393 + ], + "score": 1.0, + "content": "[2021] show that the specific random samples used for few-shot learning can change", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 390, + 505, + 403 + ], + "spans": [ + { + "bbox": [ + 106, + 390, + 435, + 403 + ], + "score": 1.0, + "content": "GPT-3’s prediction on binary and multiple choice tasks. Similarly, Wallace et al.", + "type": "text" + }, + { + "bbox": [ + 437, + 392, + 505, + 403 + ], + "score": 1.0, + "content": "[2019] show that", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 401, + 505, + 414 + ], + "spans": [ + { + "bbox": [ + 105, + 401, + 505, + 414 + ], + "score": 1.0, + "content": "innocuous prompts can routinely generate toxic model output. Our framework builds on this work by", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 412, + 505, + 426 + ], + "spans": [ + { + "bbox": [ + 105, + 412, + 505, + 426 + ], + "score": 1.0, + "content": "(i) identifying the link to cognitive biases, (ii) focusing on open-ended generation, and (iii) leveraging", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 424, + 456, + 436 + ], + "spans": [ + { + "bbox": [ + 106, + 424, + 456, + 436 + ], + "score": 1.0, + "content": "Tversky and Kahneman’s experimental methodology to elicit qualitative failure modes.", + "type": "text" + } + ], + "index": 23 + } + ], + "index": 20.5, + "bbox_fs": [ + 105, + 368, + 505, + 436 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 450, + 282, + 464 + ], + "lines": [ + { + "bbox": [ + 104, + 448, + 283, + 468 + ], + "spans": [ + { + "bbox": [ + 104, + 448, + 283, + 468 + ], + "score": 1.0, + "content": "3 Code Generation Experiments", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 24 + }, + { + "type": "title", + "bbox": [ + 106, + 474, + 162, + 486 + ], + "lines": [ + { + "bbox": [ + 105, + 474, + 162, + 487 + ], + "spans": [ + { + "bbox": [ + 105, + 474, + 162, + 487 + ], + "score": 1.0, + "content": "3.1 Models", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 25 + }, + { + "type": "text", + "bbox": [ + 106, + 494, + 505, + 539 + ], + "lines": [ + { + "bbox": [ + 105, + 494, + 507, + 508 + ], + "spans": [ + { + "bbox": [ + 105, + 494, + 507, + 508 + ], + "score": 1.0, + "content": "We study two code models: OpenAI’s Codex [Chen et al., 2021], and Salesforce’s CodeGen. [Nijkamp", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 505, + 507, + 519 + ], + "spans": [ + { + "bbox": [ + 106, + 505, + 507, + 519 + ], + "score": 1.0, + "content": "et al., 2022]. Both models are autoregressive—given a sequence of previous tokens, they predict", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 517, + 505, + 529 + ], + "spans": [ + { + "bbox": [ + 105, + 517, + 505, + 529 + ], + "score": 1.0, + "content": "the next token. Practitioners query these code models with partial programs, docstrings, or function", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 528, + 289, + 541 + ], + "spans": [ + { + "bbox": [ + 106, + 528, + 289, + 541 + ], + "score": 1.0, + "content": "signatures, and obtain completions as output.", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 27.5, + "bbox_fs": [ + 105, + 494, + 507, + 541 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 543, + 505, + 599 + ], + "lines": [ + { + "bbox": [ + 106, + 543, + 506, + 557 + ], + "spans": [ + { + "bbox": [ + 106, + 543, + 506, + 557 + ], + "score": 1.0, + "content": "Codex. We study OpenAI’s Codex, a large language model trained to generate code from docstrings", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 107, + 555, + 506, + 568 + ], + "spans": [ + { + "bbox": [ + 107, + 555, + 506, + 568 + ], + "score": 1.0, + "content": "[Chen et al., 2021]. We use the OpenAI API to query the “davinci-001” version of Codex, and use", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 565, + 506, + 579 + ], + "spans": [ + { + "bbox": [ + 105, + 565, + 506, + 579 + ], + "score": 1.0, + "content": "greedy decoding to generate solutions. Details of this model architecture are not public, but it is likely", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 576, + 505, + 590 + ], + "spans": [ + { + "bbox": [ + 105, + 576, + 505, + 590 + ], + "score": 1.0, + "content": "similar to the largest model from Chen et al. [2021]: a 12B parameter version of GPT-3 [Brown et al.,", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 106, + 587, + 369, + 599 + ], + "spans": [ + { + "bbox": [ + 106, + 587, + 369, + 599 + ], + "score": 1.0, + "content": "2020] that is fine-tuned on GitHub instead of the CommonCrawl.", + "type": "text" + } + ], + "index": 34 + } + ], + "index": 32, + "bbox_fs": [ + 105, + 543, + 506, + 599 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 604, + 505, + 638 + ], + "lines": [ + { + "bbox": [ + 106, + 604, + 505, + 616 + ], + "spans": [ + { + "bbox": [ + 106, + 604, + 505, + 616 + ], + "score": 1.0, + "content": "CodeGen. We additionally study the 6.2 billion parameter “mono” version of CodeGen, which is", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 614, + 505, + 627 + ], + "spans": [ + { + "bbox": [ + 105, + 614, + 505, + 627 + ], + "score": 1.0, + "content": "trained on text data and fine-tuned on GitHub. Unlike Codex, the weights of CodeGen are publicly", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 106, + 626, + 451, + 639 + ], + "spans": [ + { + "bbox": [ + 106, + 626, + 451, + 639 + ], + "score": 1.0, + "content": "available,2 so we run inference locally. We use greedy decoding to generate solutions.", + "type": "text" + } + ], + "index": 37 + } + ], + "index": 36, + "bbox_fs": [ + 105, + 604, + 505, + 639 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 650, + 184, + 662 + ], + "lines": [ + { + "bbox": [ + 105, + 649, + 185, + 663 + ], + "spans": [ + { + "bbox": [ + 105, + 649, + 185, + 663 + ], + "score": 1.0, + "content": "3.2 Benchmarks", + "type": "text" + } + ], + "index": 38 + } + ], + "index": 38 + }, + { + "type": "text", + "bbox": [ + 108, + 670, + 505, + 703 + ], + "lines": [ + { + "bbox": [ + 105, + 670, + 506, + 683 + ], + "spans": [ + { + "bbox": [ + 105, + 670, + 506, + 683 + ], + "score": 1.0, + "content": "In order to identify whether code models some failure mode, we need to generate prompts that elicit", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 680, + 505, + 695 + ], + "spans": [ + { + "bbox": [ + 105, + 680, + 505, + 695 + ], + "score": 1.0, + "content": "that failure. To do so, we systematically apply transformations to standard prompts. We use two", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 691, + 436, + 705 + ], + "spans": [ + { + "bbox": [ + 105, + 691, + 436, + 705 + ], + "score": 1.0, + "content": "benchmarks as sources of prompts to transform: HumanEval, and MathEquations.", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 40, + "bbox_fs": [ + 105, + 670, + 506, + 705 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "table", + "bbox": [ + 110, + 79, + 502, + 236 + ], + "blocks": [ + { + "type": "table_body", + "bbox": [ + 110, + 79, + 502, + 236 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 110, + 79, + 502, + 236 + ], + "spans": [ + { + "bbox": [ + 110, + 79, + 502, + 236 + ], + "score": 0.978, + "html": "
Framing LineModelORIGINALFRAMEDORIGINALFRAMED
raise NotImplementedCODEX32.92.41.491.7
CODEGEN25.61.50.079.3
passCoDEX32.93.09.792.7
CODEGEN25.62.10.078.7
assert FalseCODEX32.93.30.092.7
CODEGEN25.64.20.172.6
return FalseCODEX32.94.911.565.6
CODEGEN25.63.60.064.6
print("Hello world!")CODEX32.910.60.062.2
CODEGEN25.611.00.058.2
", + "type": "table", + "image_path": "a950596f8bca75dcae5dc344e398135cd6410d2b7236ca7ff919a367c6926c0f.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 110, + 79, + 502, + 131.33333333333334 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 110, + 131.33333333333334, + 502, + 183.66666666666669 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 110, + 183.66666666666669, + 502, + 236.00000000000003 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "table_footnote", + "bbox": [ + 106, + 237, + 505, + 293 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 105, + 237, + 505, + 250 + ], + "spans": [ + { + "bbox": [ + 105, + 237, + 505, + 250 + ], + "score": 1.0, + "content": "Table 1: Results of the framing experiments. We compare functional accuracy and the rate at which", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 247, + 505, + 262 + ], + "spans": [ + { + "bbox": [ + 105, + 247, + 505, + 262 + ], + "score": 1.0, + "content": "framing line is outputted over HumanEval with (framed) and without (original) irrelevant preceding", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 257, + 506, + 274 + ], + "spans": [ + { + "bbox": [ + 105, + 257, + 506, + 274 + ], + "score": 1.0, + "content": "functions. We find that the irrelevant preceding functions lower functional accuracy across all framing", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 269, + 505, + 283 + ], + "spans": [ + { + "bbox": [ + 105, + 269, + 505, + 283 + ], + "score": 1.0, + "content": "lines for Codex and CodeGen. Moreover, we find that the outputted function often appears verbatim", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 281, + 496, + 294 + ], + "spans": [ + { + "bbox": [ + 105, + 281, + 496, + 294 + ], + "score": 1.0, + "content": "in the generated output, suggesting that both models rely on irrelevant information in the prompt.", + "type": "text" + } + ], + "index": 7 + } + ], + "index": 5 + } + ], + "index": 3.0 + }, + { + "type": "text", + "bbox": [ + 106, + 321, + 505, + 378 + ], + "lines": [ + { + "bbox": [ + 106, + 321, + 504, + 335 + ], + "spans": [ + { + "bbox": [ + 106, + 321, + 504, + 335 + ], + "score": 1.0, + "content": "HumanEval. We use the HumanEval benchmark as a diverse source of “normal” prompts [Chen et al.,", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 332, + 505, + 346 + ], + "spans": [ + { + "bbox": [ + 105, + 332, + 130, + 345 + ], + "score": 0.35, + "content": "\\boxed { 2 0 2 1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 131, + 333, + 505, + 346 + ], + "score": 1.0, + "content": ". HumanEval contains 164 programming problems, each of which includes a function signature", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 343, + 505, + 357 + ], + "spans": [ + { + "bbox": [ + 105, + 343, + 505, + 357 + ], + "score": 1.0, + "content": "and a docstring. The docstring contains an English description of the desired functionality and a few", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 354, + 505, + 367 + ], + "spans": [ + { + "bbox": [ + 105, + 354, + 505, + 367 + ], + "score": 1.0, + "content": "example input-output pairs. HumanEval also contains a canonical solution for each program, which", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 365, + 440, + 379 + ], + "spans": [ + { + "bbox": [ + 106, + 366, + 177, + 379 + ], + "score": 1.0, + "content": "we use in Section", + "type": "text" + }, + { + "bbox": [ + 177, + 365, + 202, + 378 + ], + "score": 0.42, + "content": "\\underline { { \\bar { \\vert 3 . 3 . 2 \\vert } } }", + "type": "inline_equation" + }, + { + "bbox": [ + 203, + 366, + 440, + 379 + ], + "score": 1.0, + "content": "We give an example problem from HumanEval in Figure 2.", + "type": "text" + } + ], + "index": 12 + } + ], + "index": 10 + }, + { + "type": "text", + "bbox": [ + 107, + 382, + 505, + 416 + ], + "lines": [ + { + "bbox": [ + 106, + 382, + 505, + 394 + ], + "spans": [ + { + "bbox": [ + 106, + 382, + 505, + 394 + ], + "score": 1.0, + "content": "MathEquations. We also curate a set of prompts of basic arithmetic functions. For example, we", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 392, + 506, + 405 + ], + "spans": [ + { + "bbox": [ + 105, + 392, + 506, + 405 + ], + "score": 1.0, + "content": "prompt Codex to “Write a function that sums the squares of its inputs”, or “Write a function that", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 404, + 485, + 416 + ], + "spans": [ + { + "bbox": [ + 106, + 404, + 456, + 416 + ], + "score": 1.0, + "content": "sums its inputs called product_plus_five”. Further details are given in Sections 3.3.3 an", + "type": "text" + }, + { + "bbox": [ + 459, + 404, + 485, + 416 + ], + "score": 1.0, + "content": "3.3.4.", + "type": "text" + } + ], + "index": 15 + } + ], + "index": 14 + }, + { + "type": "title", + "bbox": [ + 107, + 429, + 204, + 441 + ], + "lines": [ + { + "bbox": [ + 104, + 426, + 205, + 444 + ], + "spans": [ + { + "bbox": [ + 104, + 426, + 205, + 444 + ], + "score": 1.0, + "content": "3.3 Empirical results", + "type": "text" + } + ], + "index": 16 + } + ], + "index": 16 + }, + { + "type": "text", + "bbox": [ + 106, + 449, + 505, + 538 + ], + "lines": [ + { + "bbox": [ + 106, + 450, + 506, + 462 + ], + "spans": [ + { + "bbox": [ + 106, + 450, + 506, + 462 + ], + "score": 1.0, + "content": "In this section, we show how cognitive biases can (i) inspire hypotheses for potential failure modes,", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 461, + 506, + 473 + ], + "spans": [ + { + "bbox": [ + 106, + 461, + 506, + 473 + ], + "score": 1.0, + "content": "and (ii) help us design experiments to test these hypotheses. Our approach has three steps. First,", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 472, + 506, + 484 + ], + "spans": [ + { + "bbox": [ + 105, + 472, + 506, + 484 + ], + "score": 1.0, + "content": "we construct a transformation over prompts that largely preserves semantics, but that we suspect", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 483, + 504, + 494 + ], + "spans": [ + { + "bbox": [ + 106, + 483, + 504, + 494 + ], + "score": 1.0, + "content": "will elicit a specific cognitive-bias-inspired failure mode. Next, we measure if code models are", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 493, + 505, + 505 + ], + "spans": [ + { + "bbox": [ + 105, + 493, + 505, + 505 + ], + "score": 1.0, + "content": "sensitive to the transformation, by measuring the decrease in accuracy. And finally, we check that the", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 504, + 506, + 517 + ], + "spans": [ + { + "bbox": [ + 105, + 504, + 506, + 517 + ], + "score": 1.0, + "content": "generated output has elements that are indicative of the targeted failure mode. Our approach mirrors", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 515, + 505, + 527 + ], + "spans": [ + { + "bbox": [ + 106, + 515, + 505, + 527 + ], + "score": 1.0, + "content": "the high-level methodology from Tversky and Kahneman [1974]; we empirically elicit specific failure", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 525, + 503, + 539 + ], + "spans": [ + { + "bbox": [ + 105, + 525, + 503, + 539 + ], + "score": 1.0, + "content": "modes using targeted prompts, without complete mechanistic insight into the system that we study.", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 20.5 + }, + { + "type": "text", + "bbox": [ + 107, + 542, + 506, + 587 + ], + "lines": [ + { + "bbox": [ + 105, + 541, + 502, + 555 + ], + "spans": [ + { + "bbox": [ + 105, + 541, + 502, + 555 + ], + "score": 1.0, + "content": "We draw inspiration from four cognitive biases: the framing effect (Tversky and Kahneman [1981]", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 551, + 506, + 566 + ], + "spans": [ + { + "bbox": [ + 105, + 551, + 375, + 566 + ], + "score": 1.0, + "content": "Section 3.3.1), anchoring (Tversky and Kahneman [1974]; Section", + "type": "text" + }, + { + "bbox": [ + 376, + 552, + 401, + 565 + ], + "score": 0.51, + "content": "\\underline { { \\overline { { | 3 . 3 . 2 ) } } } }", + "type": "inline_equation" + }, + { + "bbox": [ + 402, + 551, + 506, + 566 + ], + "score": 1.0, + "content": ", the availability heuristic", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 107, + 564, + 506, + 577 + ], + "spans": [ + { + "bbox": [ + 107, + 564, + 506, + 577 + ], + "score": 1.0, + "content": "(Tversky and Kahneman [1973]; Section 3.3.3), and attribute substitution (Kahneman and Frederick", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 107, + 574, + 196, + 588 + ], + "spans": [ + { + "bbox": [ + 107, + 574, + 196, + 588 + ], + "score": 1.0, + "content": "[2002]; Section 3.3.4).", + "type": "text" + } + ], + "index": 28 + } + ], + "index": 26.5 + }, + { + "type": "title", + "bbox": [ + 107, + 599, + 255, + 611 + ], + "lines": [ + { + "bbox": [ + 105, + 597, + 256, + 613 + ], + "spans": [ + { + "bbox": [ + 105, + 597, + 256, + 613 + ], + "score": 1.0, + "content": "3.3.1 Inspiration: Framing effect", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 29 + }, + { + "type": "text", + "bbox": [ + 107, + 617, + 505, + 684 + ], + "lines": [ + { + "bbox": [ + 105, + 617, + 505, + 630 + ], + "spans": [ + { + "bbox": [ + 105, + 617, + 505, + 630 + ], + "score": 1.0, + "content": "We first draw inspiration from the framing effect: predictable shifts in human responses when the", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 627, + 505, + 642 + ], + "spans": [ + { + "bbox": [ + 105, + 627, + 505, + 642 + ], + "score": 1.0, + "content": "same problem is framed in different ways [Tversky and Kahneman, 1981]. In their study identifying", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 640, + 505, + 652 + ], + "spans": [ + { + "bbox": [ + 106, + 640, + 249, + 651 + ], + "score": 1.0, + "content": "the effect, Tversky and Kahneman", + "type": "text" + }, + { + "bbox": [ + 250, + 640, + 505, + 652 + ], + "score": 1.0, + "content": "[1981] find that subjects favor certainly saving 200 people over", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 650, + 505, + 663 + ], + "spans": [ + { + "bbox": [ + 105, + 650, + 219, + 663 + ], + "score": 1.0, + "content": "saving 600 with probability", + "type": "text" + }, + { + "bbox": [ + 219, + 651, + 232, + 661 + ], + "score": 0.42, + "content": "\\overline { { 1 / 3 } }", + "type": "inline_equation" + }, + { + "bbox": [ + 233, + 650, + 505, + 663 + ], + "score": 1.0, + "content": ", yet prefer losing 600 with probability 2/3 over certainly losing 400", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 661, + 505, + 675 + ], + "spans": [ + { + "bbox": [ + 105, + 661, + 505, + 675 + ], + "score": 1.0, + "content": "(even though these are equivalent). At its core, the framing effect shows how humans can rely on", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 673, + 358, + 685 + ], + "spans": [ + { + "bbox": [ + 105, + 673, + 358, + 685 + ], + "score": 1.0, + "content": "semantically irrelevant information when they make decisions.", + "type": "text" + } + ], + "index": 35 + } + ], + "index": 32.5 + }, + { + "type": "text", + "bbox": [ + 108, + 689, + 505, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 689, + 505, + 702 + ], + "spans": [ + { + "bbox": [ + 106, + 689, + 505, + 702 + ], + "score": 1.0, + "content": "Using the framing effect as inspiration, we hypothesize that code generation models may generate", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "score": 1.0, + "content": "solutions exclusively from irrelevant information in the prompt. To elicit this failure, we transform", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 710, + 505, + 724 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 505, + 724 + ], + "score": 1.0, + "content": "HumanEval prompts by prepending irrelevant preceding functions. Specifically, to generate irrelevant", + "type": "text" + } + ], + "index": 38 + } + ], + "index": 37 + } + ], + "page_idx": 3, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 302, + 742, + 308, + 750 + ], + "lines": [ + { + "bbox": [ + 301, + 741, + 310, + 752 + ], + "spans": [ + { + "bbox": [ + 301, + 741, + 310, + 752 + ], + "score": 1.0, + "content": "", + "type": "text", + "height": 11, + "width": 9 + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "table", + "bbox": [ + 110, + 79, + 502, + 236 + ], + "blocks": [ + { + "type": "table_body", + "bbox": [ + 110, + 79, + 502, + 236 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 110, + 79, + 502, + 236 + ], + "spans": [ + { + "bbox": [ + 110, + 79, + 502, + 236 + ], + "score": 0.978, + "html": "
Framing LineModelORIGINALFRAMEDORIGINALFRAMED
raise NotImplementedCODEX32.92.41.491.7
CODEGEN25.61.50.079.3
passCoDEX32.93.09.792.7
CODEGEN25.62.10.078.7
assert FalseCODEX32.93.30.092.7
CODEGEN25.64.20.172.6
return FalseCODEX32.94.911.565.6
CODEGEN25.63.60.064.6
print("Hello world!")CODEX32.910.60.062.2
CODEGEN25.611.00.058.2
", + "type": "table", + "image_path": "a950596f8bca75dcae5dc344e398135cd6410d2b7236ca7ff919a367c6926c0f.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 110, + 79, + 502, + 131.33333333333334 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 110, + 131.33333333333334, + 502, + 183.66666666666669 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 110, + 183.66666666666669, + 502, + 236.00000000000003 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "table_footnote", + "bbox": [ + 106, + 237, + 505, + 293 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 105, + 237, + 505, + 250 + ], + "spans": [ + { + "bbox": [ + 105, + 237, + 505, + 250 + ], + "score": 1.0, + "content": "Table 1: Results of the framing experiments. We compare functional accuracy and the rate at which", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 247, + 505, + 262 + ], + "spans": [ + { + "bbox": [ + 105, + 247, + 505, + 262 + ], + "score": 1.0, + "content": "framing line is outputted over HumanEval with (framed) and without (original) irrelevant preceding", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 257, + 506, + 274 + ], + "spans": [ + { + "bbox": [ + 105, + 257, + 506, + 274 + ], + "score": 1.0, + "content": "functions. We find that the irrelevant preceding functions lower functional accuracy across all framing", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 269, + 505, + 283 + ], + "spans": [ + { + "bbox": [ + 105, + 269, + 505, + 283 + ], + "score": 1.0, + "content": "lines for Codex and CodeGen. Moreover, we find that the outputted function often appears verbatim", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 281, + 496, + 294 + ], + "spans": [ + { + "bbox": [ + 105, + 281, + 496, + 294 + ], + "score": 1.0, + "content": "in the generated output, suggesting that both models rely on irrelevant information in the prompt.", + "type": "text" + } + ], + "index": 7 + } + ], + "index": 5 + } + ], + "index": 3.0 + }, + { + "type": "text", + "bbox": [ + 106, + 321, + 505, + 378 + ], + "lines": [ + { + "bbox": [ + 106, + 321, + 504, + 335 + ], + "spans": [ + { + "bbox": [ + 106, + 321, + 504, + 335 + ], + "score": 1.0, + "content": "HumanEval. We use the HumanEval benchmark as a diverse source of “normal” prompts [Chen et al.,", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 332, + 505, + 346 + ], + "spans": [ + { + "bbox": [ + 105, + 332, + 130, + 345 + ], + "score": 0.35, + "content": "\\boxed { 2 0 2 1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 131, + 333, + 505, + 346 + ], + "score": 1.0, + "content": ". HumanEval contains 164 programming problems, each of which includes a function signature", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 343, + 505, + 357 + ], + "spans": [ + { + "bbox": [ + 105, + 343, + 505, + 357 + ], + "score": 1.0, + "content": "and a docstring. The docstring contains an English description of the desired functionality and a few", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 354, + 505, + 367 + ], + "spans": [ + { + "bbox": [ + 105, + 354, + 505, + 367 + ], + "score": 1.0, + "content": "example input-output pairs. HumanEval also contains a canonical solution for each program, which", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 365, + 440, + 379 + ], + "spans": [ + { + "bbox": [ + 106, + 366, + 177, + 379 + ], + "score": 1.0, + "content": "we use in Section", + "type": "text" + }, + { + "bbox": [ + 177, + 365, + 202, + 378 + ], + "score": 0.42, + "content": "\\underline { { \\bar { \\vert 3 . 3 . 2 \\vert } } }", + "type": "inline_equation" + }, + { + "bbox": [ + 203, + 366, + 440, + 379 + ], + "score": 1.0, + "content": "We give an example problem from HumanEval in Figure 2.", + "type": "text" + } + ], + "index": 12 + } + ], + "index": 10, + "bbox_fs": [ + 105, + 321, + 505, + 379 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 382, + 505, + 416 + ], + "lines": [ + { + "bbox": [ + 106, + 382, + 505, + 394 + ], + "spans": [ + { + "bbox": [ + 106, + 382, + 505, + 394 + ], + "score": 1.0, + "content": "MathEquations. We also curate a set of prompts of basic arithmetic functions. For example, we", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 392, + 506, + 405 + ], + "spans": [ + { + "bbox": [ + 105, + 392, + 506, + 405 + ], + "score": 1.0, + "content": "prompt Codex to “Write a function that sums the squares of its inputs”, or “Write a function that", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 404, + 485, + 416 + ], + "spans": [ + { + "bbox": [ + 106, + 404, + 456, + 416 + ], + "score": 1.0, + "content": "sums its inputs called product_plus_five”. Further details are given in Sections 3.3.3 an", + "type": "text" + }, + { + "bbox": [ + 459, + 404, + 485, + 416 + ], + "score": 1.0, + "content": "3.3.4.", + "type": "text" + } + ], + "index": 15 + } + ], + "index": 14, + "bbox_fs": [ + 105, + 382, + 506, + 416 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 429, + 204, + 441 + ], + "lines": [ + { + "bbox": [ + 104, + 426, + 205, + 444 + ], + "spans": [ + { + "bbox": [ + 104, + 426, + 205, + 444 + ], + "score": 1.0, + "content": "3.3 Empirical results", + "type": "text" + } + ], + "index": 16 + } + ], + "index": 16 + }, + { + "type": "text", + "bbox": [ + 106, + 449, + 505, + 538 + ], + "lines": [ + { + "bbox": [ + 106, + 450, + 506, + 462 + ], + "spans": [ + { + "bbox": [ + 106, + 450, + 506, + 462 + ], + "score": 1.0, + "content": "In this section, we show how cognitive biases can (i) inspire hypotheses for potential failure modes,", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 461, + 506, + 473 + ], + "spans": [ + { + "bbox": [ + 106, + 461, + 506, + 473 + ], + "score": 1.0, + "content": "and (ii) help us design experiments to test these hypotheses. Our approach has three steps. First,", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 472, + 506, + 484 + ], + "spans": [ + { + "bbox": [ + 105, + 472, + 506, + 484 + ], + "score": 1.0, + "content": "we construct a transformation over prompts that largely preserves semantics, but that we suspect", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 483, + 504, + 494 + ], + "spans": [ + { + "bbox": [ + 106, + 483, + 504, + 494 + ], + "score": 1.0, + "content": "will elicit a specific cognitive-bias-inspired failure mode. Next, we measure if code models are", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 493, + 505, + 505 + ], + "spans": [ + { + "bbox": [ + 105, + 493, + 505, + 505 + ], + "score": 1.0, + "content": "sensitive to the transformation, by measuring the decrease in accuracy. And finally, we check that the", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 504, + 506, + 517 + ], + "spans": [ + { + "bbox": [ + 105, + 504, + 506, + 517 + ], + "score": 1.0, + "content": "generated output has elements that are indicative of the targeted failure mode. Our approach mirrors", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 515, + 505, + 527 + ], + "spans": [ + { + "bbox": [ + 106, + 515, + 505, + 527 + ], + "score": 1.0, + "content": "the high-level methodology from Tversky and Kahneman [1974]; we empirically elicit specific failure", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 525, + 503, + 539 + ], + "spans": [ + { + "bbox": [ + 105, + 525, + 503, + 539 + ], + "score": 1.0, + "content": "modes using targeted prompts, without complete mechanistic insight into the system that we study.", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 20.5, + "bbox_fs": [ + 105, + 450, + 506, + 539 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 542, + 506, + 587 + ], + "lines": [ + { + "bbox": [ + 105, + 541, + 502, + 555 + ], + "spans": [ + { + "bbox": [ + 105, + 541, + 502, + 555 + ], + "score": 1.0, + "content": "We draw inspiration from four cognitive biases: the framing effect (Tversky and Kahneman [1981]", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 551, + 506, + 566 + ], + "spans": [ + { + "bbox": [ + 105, + 551, + 375, + 566 + ], + "score": 1.0, + "content": "Section 3.3.1), anchoring (Tversky and Kahneman [1974]; Section", + "type": "text" + }, + { + "bbox": [ + 376, + 552, + 401, + 565 + ], + "score": 0.51, + "content": "\\underline { { \\overline { { | 3 . 3 . 2 ) } } } }", + "type": "inline_equation" + }, + { + "bbox": [ + 402, + 551, + 506, + 566 + ], + "score": 1.0, + "content": ", the availability heuristic", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 107, + 564, + 506, + 577 + ], + "spans": [ + { + "bbox": [ + 107, + 564, + 506, + 577 + ], + "score": 1.0, + "content": "(Tversky and Kahneman [1973]; Section 3.3.3), and attribute substitution (Kahneman and Frederick", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 107, + 574, + 196, + 588 + ], + "spans": [ + { + "bbox": [ + 107, + 574, + 196, + 588 + ], + "score": 1.0, + "content": "[2002]; Section 3.3.4).", + "type": "text" + } + ], + "index": 28 + } + ], + "index": 26.5, + "bbox_fs": [ + 105, + 541, + 506, + 588 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 599, + 255, + 611 + ], + "lines": [ + { + "bbox": [ + 105, + 597, + 256, + 613 + ], + "spans": [ + { + "bbox": [ + 105, + 597, + 256, + 613 + ], + "score": 1.0, + "content": "3.3.1 Inspiration: Framing effect", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 29 + }, + { + "type": "text", + "bbox": [ + 107, + 617, + 505, + 684 + ], + "lines": [ + { + "bbox": [ + 105, + 617, + 505, + 630 + ], + "spans": [ + { + "bbox": [ + 105, + 617, + 505, + 630 + ], + "score": 1.0, + "content": "We first draw inspiration from the framing effect: predictable shifts in human responses when the", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 627, + 505, + 642 + ], + "spans": [ + { + "bbox": [ + 105, + 627, + 505, + 642 + ], + "score": 1.0, + "content": "same problem is framed in different ways [Tversky and Kahneman, 1981]. In their study identifying", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 640, + 505, + 652 + ], + "spans": [ + { + "bbox": [ + 106, + 640, + 249, + 651 + ], + "score": 1.0, + "content": "the effect, Tversky and Kahneman", + "type": "text" + }, + { + "bbox": [ + 250, + 640, + 505, + 652 + ], + "score": 1.0, + "content": "[1981] find that subjects favor certainly saving 200 people over", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 650, + 505, + 663 + ], + "spans": [ + { + "bbox": [ + 105, + 650, + 219, + 663 + ], + "score": 1.0, + "content": "saving 600 with probability", + "type": "text" + }, + { + "bbox": [ + 219, + 651, + 232, + 661 + ], + "score": 0.42, + "content": "\\overline { { 1 / 3 } }", + "type": "inline_equation" + }, + { + "bbox": [ + 233, + 650, + 505, + 663 + ], + "score": 1.0, + "content": ", yet prefer losing 600 with probability 2/3 over certainly losing 400", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 661, + 505, + 675 + ], + "spans": [ + { + "bbox": [ + 105, + 661, + 505, + 675 + ], + "score": 1.0, + "content": "(even though these are equivalent). At its core, the framing effect shows how humans can rely on", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 673, + 358, + 685 + ], + "spans": [ + { + "bbox": [ + 105, + 673, + 358, + 685 + ], + "score": 1.0, + "content": "semantically irrelevant information when they make decisions.", + "type": "text" + } + ], + "index": 35 + } + ], + "index": 32.5, + "bbox_fs": [ + 105, + 617, + 505, + 685 + ] + }, + { + "type": "text", + "bbox": [ + 108, + 689, + 505, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 689, + 505, + 702 + ], + "spans": [ + { + "bbox": [ + 106, + 689, + 505, + 702 + ], + "score": 1.0, + "content": "Using the framing effect as inspiration, we hypothesize that code generation models may generate", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "score": 1.0, + "content": "solutions exclusively from irrelevant information in the prompt. To elicit this failure, we transform", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 710, + 505, + 724 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 505, + 724 + ], + "score": 1.0, + "content": "HumanEval prompts by prepending irrelevant preceding functions. Specifically, to generate irrelevant", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 106, + 221, + 505, + 232 + ], + "spans": [ + { + "bbox": [ + 106, + 221, + 505, + 232 + ], + "score": 1.0, + "content": "preceding functions, we combine a random prompt from HumanEval with a framing line. We test", + "type": "text", + "cross_page": true + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 230, + 506, + 244 + ], + "spans": [ + { + "bbox": [ + 105, + 230, + 506, + 244 + ], + "score": 1.0, + "content": "five framing lines: raise NotImplementedError , pass , assert False , return False , and", + "type": "text", + "cross_page": true + } + ], + "index": 10 + }, + { + "bbox": [ + 104, + 241, + 506, + 255 + ], + "spans": [ + { + "bbox": [ + 104, + 241, + 506, + 255 + ], + "score": 1.0, + "content": "print(\"Hello world!\") . We first check that prepending these irrelevant preceding functions", + "type": "text", + "cross_page": true + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 252, + 506, + 267 + ], + "spans": [ + { + "bbox": [ + 105, + 252, + 506, + 267 + ], + "score": 1.0, + "content": "decreases functional accuracy.3 Next, to test if models relied on irrelevant information in the prompt,", + "type": "text", + "cross_page": true + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 263, + 499, + 277 + ], + "spans": [ + { + "bbox": [ + 105, + 263, + 499, + 277 + ], + "score": 1.0, + "content": "we measure how much more frequently the framing line appears verbatim in the generated output.", + "type": "text", + "cross_page": true + } + ], + "index": 13 + } + ], + "index": 37, + "bbox_fs": [ + 105, + 689, + 505, + 724 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "image", + "bbox": [ + 109, + 69, + 502, + 126 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 109, + 69, + 502, + 126 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 109, + 69, + 502, + 126 + ], + "spans": [ + { + "bbox": [ + 109, + 69, + 502, + 126 + ], + "score": 0.937, + "type": "image", + "image_path": "3ca12ceac2dfdc76f5b5cb25925443fec2d6975fface7597f9f0a393592f8cfd.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 109, + 69, + 502, + 88.0 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 109, + 88.0, + 502, + 107.0 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 109, + 107.0, + 502, + 126.0 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 131, + 505, + 197 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 105, + 130, + 506, + 144 + ], + "spans": [ + { + "bbox": [ + 105, + 130, + 506, + 144 + ], + "score": 1.0, + "content": "Figure 3: Illustration of our anchoring experiment using a real example (expanded in Figure 7). We", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 142, + 506, + 155 + ], + "spans": [ + { + "bbox": [ + 105, + 142, + 506, + 155 + ], + "score": 1.0, + "content": "construct the anchor function (left) by taking the function signature from the HumanEval prompt", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 153, + 506, + 165 + ], + "spans": [ + { + "bbox": [ + 105, + 153, + 180, + 165 + ], + "score": 1.0, + "content": "(blue), appending", + "type": "text" + }, + { + "bbox": [ + 180, + 155, + 188, + 163 + ], + "score": 0.62, + "content": "n", + "type": "inline_equation" + }, + { + "bbox": [ + 188, + 153, + 506, + 165 + ], + "score": 1.0, + "content": "lines of the canonical solution (green), then adding anchoring lines (red). We", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 164, + 506, + 177 + ], + "spans": [ + { + "bbox": [ + 105, + 164, + 506, + 177 + ], + "score": 1.0, + "content": "construct the full prompt (center) by combining the anchor function, the original HumanEval prompt,", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 176, + 505, + 187 + ], + "spans": [ + { + "bbox": [ + 106, + 176, + 154, + 187 + ], + "score": 1.0, + "content": "and the first", + "type": "text" + }, + { + "bbox": [ + 154, + 177, + 162, + 185 + ], + "score": 0.75, + "content": "n", + "type": "inline_equation" + }, + { + "bbox": [ + 162, + 176, + 505, + 187 + ], + "score": 1.0, + "content": "lines of the canonical solution. The solution Codex generates (right) combines elements", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 185, + 501, + 199 + ], + "spans": [ + { + "bbox": [ + 105, + 185, + 501, + 199 + ], + "score": 1.0, + "content": "of a canonical solution (checks condition and adds to ret.), with the anchor function (for var loop).", + "type": "text" + } + ], + "index": 8 + } + ], + "index": 5.5 + } + ], + "index": 3.25 + }, + { + "type": "text", + "bbox": [ + 106, + 219, + 505, + 275 + ], + "lines": [ + { + "bbox": [ + 106, + 221, + 505, + 232 + ], + "spans": [ + { + "bbox": [ + 106, + 221, + 505, + 232 + ], + "score": 1.0, + "content": "preceding functions, we combine a random prompt from HumanEval with a framing line. We test", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 230, + 506, + 244 + ], + "spans": [ + { + "bbox": [ + 105, + 230, + 506, + 244 + ], + "score": 1.0, + "content": "five framing lines: raise NotImplementedError , pass , assert False , return False , and", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 104, + 241, + 506, + 255 + ], + "spans": [ + { + "bbox": [ + 104, + 241, + 506, + 255 + ], + "score": 1.0, + "content": "print(\"Hello world!\") . We first check that prepending these irrelevant preceding functions", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 252, + 506, + 267 + ], + "spans": [ + { + "bbox": [ + 105, + 252, + 506, + 267 + ], + "score": 1.0, + "content": "decreases functional accuracy.3 Next, to test if models relied on irrelevant information in the prompt,", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 263, + 499, + 277 + ], + "spans": [ + { + "bbox": [ + 105, + 263, + 499, + 277 + ], + "score": 1.0, + "content": "we measure how much more frequently the framing line appears verbatim in the generated output.", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 11 + }, + { + "type": "text", + "bbox": [ + 106, + 280, + 505, + 357 + ], + "lines": [ + { + "bbox": [ + 105, + 279, + 505, + 294 + ], + "spans": [ + { + "bbox": [ + 105, + 279, + 505, + 294 + ], + "score": 1.0, + "content": "We report the results of our framing experiments in Table 1. We find that adding irrelevant preceding", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 291, + 506, + 304 + ], + "spans": [ + { + "bbox": [ + 106, + 291, + 506, + 304 + ], + "score": 1.0, + "content": "functions consistently lowers functional accuracy, by between 22.3 and 30.5 points for Codex, across", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 106, + 302, + 506, + 315 + ], + "spans": [ + { + "bbox": [ + 106, + 302, + 506, + 315 + ], + "score": 1.0, + "content": "the different framing lines we tested. Moreover, both models frequently generate the framing line:", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 106, + 313, + 505, + 326 + ], + "spans": [ + { + "bbox": [ + 106, + 313, + 127, + 324 + ], + "score": 0.86, + "content": "81 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 127, + 313, + 240, + 326 + ], + "score": 1.0, + "content": "of the time for Codex and", + "type": "text" + }, + { + "bbox": [ + 241, + 313, + 268, + 324 + ], + "score": 0.88, + "content": "7 0 . 7 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 269, + 313, + 439, + 326 + ], + "score": 1.0, + "content": "of time for CodeGen, compared to only", + "type": "text" + }, + { + "bbox": [ + 439, + 313, + 462, + 324 + ], + "score": 0.87, + "content": "4 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 462, + 313, + 482, + 326 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 482, + 313, + 505, + 324 + ], + "score": 0.85, + "content": "0 . 0 \\%", + "type": "inline_equation" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 324, + 505, + 337 + ], + "spans": [ + { + "bbox": [ + 106, + 324, + 505, + 337 + ], + "score": 1.0, + "content": "over untransformed prompts respectively. These results suggest that code generation models can", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 106, + 336, + 505, + 347 + ], + "spans": [ + { + "bbox": [ + 106, + 336, + 505, + 347 + ], + "score": 1.0, + "content": "erroneously rely on irrelevant information in the prompt in predictable ways, even in the extreme", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 346, + 497, + 358 + ], + "spans": [ + { + "bbox": [ + 106, + 346, + 497, + 358 + ], + "score": 1.0, + "content": "case when doing so contradicts the type specification in the function signature (return False ).", + "type": "text" + } + ], + "index": 20 + } + ], + "index": 17 + }, + { + "type": "title", + "bbox": [ + 107, + 370, + 238, + 382 + ], + "lines": [ + { + "bbox": [ + 105, + 367, + 239, + 386 + ], + "spans": [ + { + "bbox": [ + 105, + 367, + 239, + 386 + ], + "score": 1.0, + "content": "3.3.2 Inspiration: Anchoring", + "type": "text" + } + ], + "index": 21 + } + ], + "index": 21 + }, + { + "type": "text", + "bbox": [ + 107, + 389, + 505, + 445 + ], + "lines": [ + { + "bbox": [ + 106, + 389, + 505, + 401 + ], + "spans": [ + { + "bbox": [ + 106, + 389, + 505, + 401 + ], + "score": 1.0, + "content": "We next draw inspiration from anchoring: humans’ tendency to insufficiently adjust their estimates", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 400, + 505, + 413 + ], + "spans": [ + { + "bbox": [ + 105, + 400, + 368, + 413 + ], + "score": 1.0, + "content": "away from initial values. For example, Tversky and Kahneman", + "type": "text" + }, + { + "bbox": [ + 368, + 400, + 396, + 412 + ], + "score": 0.57, + "content": "\\mathbb { \\underline { { \\lVert \\mathbf { 9 7 4 } } \\rVert } }", + "type": "inline_equation" + }, + { + "bbox": [ + 396, + 400, + 505, + 413 + ], + "score": 1.0, + "content": "find that subjects’ median", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 411, + 505, + 423 + ], + "spans": [ + { + "bbox": [ + 105, + 411, + 377, + 423 + ], + "score": 1.0, + "content": "estimate for the fraction of African countries in the UN shifts from", + "type": "text" + }, + { + "bbox": [ + 378, + 412, + 397, + 422 + ], + "score": 0.83, + "content": "2 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 398, + 411, + 408, + 423 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 408, + 412, + 428, + 422 + ], + "score": 0.87, + "content": "45 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 429, + 411, + 505, + 423 + ], + "score": 1.0, + "content": ", based on whether", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 420, + 506, + 436 + ], + "spans": [ + { + "bbox": [ + 105, + 420, + 345, + 436 + ], + "score": 1.0, + "content": "they were first asked if the fraction was greater or less than", + "type": "text" + }, + { + "bbox": [ + 345, + 423, + 365, + 433 + ], + "score": 0.88, + "content": "10 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 365, + 420, + 383, + 436 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 383, + 423, + 403, + 433 + ], + "score": 0.87, + "content": "65 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 403, + 420, + 506, + 436 + ], + "score": 1.0, + "content": ", respectively. Anchoring", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 433, + 497, + 446 + ], + "spans": [ + { + "bbox": [ + 105, + 433, + 497, + 446 + ], + "score": 1.0, + "content": "captures how humans adjust to partial information, versus irrelevant information (framing effect).", + "type": "text" + } + ], + "index": 26 + } + ], + "index": 24 + }, + { + "type": "text", + "bbox": [ + 107, + 450, + 505, + 516 + ], + "lines": [ + { + "bbox": [ + 106, + 449, + 505, + 462 + ], + "spans": [ + { + "bbox": [ + 106, + 449, + 505, + 462 + ], + "score": 1.0, + "content": "Using anchoring as inspiration, we hypothesize that code generation models may adjust their output", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 106, + 460, + 505, + 473 + ], + "spans": [ + { + "bbox": [ + 106, + 460, + 505, + 473 + ], + "score": 1.0, + "content": "towards related solutions, when these solutions are included in the prompt. To elicit this failure, we", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 104, + 471, + 506, + 484 + ], + "spans": [ + { + "bbox": [ + 104, + 471, + 506, + 484 + ], + "score": 1.0, + "content": "prepend anchor functions to prompts: functions that are similar to a valid solution for a HumanEval", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 482, + 506, + 495 + ], + "spans": [ + { + "bbox": [ + 105, + 482, + 506, + 495 + ], + "score": 1.0, + "content": "prompt, but contain some error. We first check that prepending these anchor functions decreases", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 493, + 505, + 506 + ], + "spans": [ + { + "bbox": [ + 105, + 493, + 243, + 506 + ], + "score": 1.0, + "content": "functional accuracy, as in Section", + "type": "text" + }, + { + "bbox": [ + 244, + 493, + 269, + 506 + ], + "score": 0.48, + "content": "3 . 3 . 1 .", + "type": "inline_equation" + }, + { + "bbox": [ + 270, + 493, + 505, + 506 + ], + "score": 1.0, + "content": "Next, to test if models adjust their output towards related", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 505, + 462, + 516 + ], + "spans": [ + { + "bbox": [ + 106, + 505, + 462, + 516 + ], + "score": 1.0, + "content": "solutions, we check that the generated solution contains elements of the anchor function.", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 29.5 + }, + { + "type": "text", + "bbox": [ + 107, + 520, + 505, + 565 + ], + "lines": [ + { + "bbox": [ + 106, + 521, + 504, + 532 + ], + "spans": [ + { + "bbox": [ + 106, + 521, + 504, + 532 + ], + "score": 1.0, + "content": "We aim to construct anchor functions that are similar to functions in HumanEval prompts and that", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 532, + 505, + 543 + ], + "spans": [ + { + "bbox": [ + 105, + 532, + 505, + 543 + ], + "score": 1.0, + "content": "compile, but are incorrect. To do so, we take a prefix of the canonical solution, then add additional", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 542, + 505, + 555 + ], + "spans": [ + { + "bbox": [ + 105, + 543, + 331, + 555 + ], + "score": 1.0, + "content": "anchor lines that produce an incorrect output. See Figure", + "type": "text" + }, + { + "bbox": [ + 331, + 542, + 340, + 555 + ], + "score": 0.56, + "content": "\\triangledown", + "type": "inline_equation" + }, + { + "bbox": [ + 341, + 543, + 505, + 555 + ], + "score": 1.0, + "content": "for an example. We describe two types of", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 552, + 507, + 567 + ], + "spans": [ + { + "bbox": [ + 105, + 552, + 507, + 567 + ], + "score": 1.0, + "content": "anchor lines, and how we test their influence on the generated solutions, in the following paragraphs.", + "type": "text" + } + ], + "index": 36 + } + ], + "index": 34.5 + }, + { + "type": "text", + "bbox": [ + 107, + 570, + 505, + 603 + ], + "lines": [ + { + "bbox": [ + 106, + 570, + 505, + 582 + ], + "spans": [ + { + "bbox": [ + 106, + 570, + 505, + 582 + ], + "score": 1.0, + "content": "Print-var anchor lines. We first study print-var anchor lines, which iterate over all variables in the", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 581, + 505, + 592 + ], + "spans": [ + { + "bbox": [ + 106, + 581, + 505, + 592 + ], + "score": 1.0, + "content": "function signature and print their values. For a function with inputs var1 and var2 , the associated", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 592, + 212, + 603 + ], + "spans": [ + { + "bbox": [ + 105, + 592, + 212, + 603 + ], + "score": 1.0, + "content": "print-var anchor lines are:", + "type": "text" + } + ], + "index": 39 + } + ], + "index": 38 + }, + { + "type": "text", + "bbox": [ + 107, + 608, + 232, + 631 + ], + "lines": [ + { + "bbox": [ + 106, + 608, + 233, + 620 + ], + "spans": [ + { + "bbox": [ + 106, + 608, + 233, + 620 + ], + "score": 1.0, + "content": "for var in [var1, var2]:", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 126, + 620, + 181, + 631 + ], + "spans": [ + { + "bbox": [ + 126, + 620, + 181, + 631 + ], + "score": 1.0, + "content": "print(var)", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 40.5 + }, + { + "type": "text", + "bbox": [ + 106, + 636, + 504, + 659 + ], + "lines": [ + { + "bbox": [ + 106, + 636, + 505, + 649 + ], + "spans": [ + { + "bbox": [ + 106, + 636, + 505, + 649 + ], + "score": 1.0, + "content": "To study the influence of the print-var anchor lines on the solution, we measure how often (i) just the", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 646, + 497, + 660 + ], + "spans": [ + { + "bbox": [ + 105, + 646, + 497, + 660 + ], + "score": 1.0, + "content": "first line (for loop), and (ii) just the second line (print statement) appear in the generated solution.", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 42.5 + }, + { + "type": "text", + "bbox": [ + 107, + 663, + 505, + 696 + ], + "lines": [ + { + "bbox": [ + 105, + 663, + 505, + 675 + ], + "spans": [ + { + "bbox": [ + 105, + 663, + 505, + 675 + ], + "score": 1.0, + "content": "Add-var anchor lines. We also study add-var anchor lines, which return the sum of all variables in", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 675, + 505, + 686 + ], + "spans": [ + { + "bbox": [ + 106, + 675, + 505, + 686 + ], + "score": 1.0, + "content": "the function signature (converted to strings). For a function with inputs var1 and var2 , the add-var", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 684, + 176, + 698 + ], + "spans": [ + { + "bbox": [ + 105, + 684, + 176, + 698 + ], + "score": 1.0, + "content": "anchor lines are:", + "type": "text" + } + ], + "index": 46 + } + ], + "index": 45 + } + ], + "page_idx": 4, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 105, + 700, + 504, + 723 + ], + "lines": [ + { + "bbox": [ + 118, + 698, + 505, + 714 + ], + "spans": [ + { + "bbox": [ + 118, + 698, + 505, + 714 + ], + "score": 1.0, + "content": "3 Following Chen et al. [2021], we measure performance on HumanEval with functional accuracy: the fraction", + "type": "text" + } + ] + }, + { + "bbox": [ + 106, + 711, + 502, + 723 + ], + "spans": [ + { + "bbox": [ + 106, + 711, + 502, + 723 + ], + "score": 1.0, + "content": "of programs that pass all of the test cases provided at the url: https://github.com/openai/human-eval.", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 302, + 741, + 309, + 750 + ], + "lines": [ + { + "bbox": [ + 301, + 740, + 310, + 753 + ], + "spans": [ + { + "bbox": [ + 301, + 740, + 310, + 753 + ], + "score": 1.0, + "content": "5", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "image", + "bbox": [ + 109, + 69, + 502, + 126 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 109, + 69, + 502, + 126 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 109, + 69, + 502, + 126 + ], + "spans": [ + { + "bbox": [ + 109, + 69, + 502, + 126 + ], + "score": 0.937, + "type": "image", + "image_path": "3ca12ceac2dfdc76f5b5cb25925443fec2d6975fface7597f9f0a393592f8cfd.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 109, + 69, + 502, + 88.0 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 109, + 88.0, + 502, + 107.0 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 109, + 107.0, + 502, + 126.0 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 131, + 505, + 197 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 105, + 130, + 506, + 144 + ], + "spans": [ + { + "bbox": [ + 105, + 130, + 506, + 144 + ], + "score": 1.0, + "content": "Figure 3: Illustration of our anchoring experiment using a real example (expanded in Figure 7). We", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 142, + 506, + 155 + ], + "spans": [ + { + "bbox": [ + 105, + 142, + 506, + 155 + ], + "score": 1.0, + "content": "construct the anchor function (left) by taking the function signature from the HumanEval prompt", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 153, + 506, + 165 + ], + "spans": [ + { + "bbox": [ + 105, + 153, + 180, + 165 + ], + "score": 1.0, + "content": "(blue), appending", + "type": "text" + }, + { + "bbox": [ + 180, + 155, + 188, + 163 + ], + "score": 0.62, + "content": "n", + "type": "inline_equation" + }, + { + "bbox": [ + 188, + 153, + 506, + 165 + ], + "score": 1.0, + "content": "lines of the canonical solution (green), then adding anchoring lines (red). We", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 164, + 506, + 177 + ], + "spans": [ + { + "bbox": [ + 105, + 164, + 506, + 177 + ], + "score": 1.0, + "content": "construct the full prompt (center) by combining the anchor function, the original HumanEval prompt,", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 176, + 505, + 187 + ], + "spans": [ + { + "bbox": [ + 106, + 176, + 154, + 187 + ], + "score": 1.0, + "content": "and the first", + "type": "text" + }, + { + "bbox": [ + 154, + 177, + 162, + 185 + ], + "score": 0.75, + "content": "n", + "type": "inline_equation" + }, + { + "bbox": [ + 162, + 176, + 505, + 187 + ], + "score": 1.0, + "content": "lines of the canonical solution. The solution Codex generates (right) combines elements", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 185, + 501, + 199 + ], + "spans": [ + { + "bbox": [ + 105, + 185, + 501, + 199 + ], + "score": 1.0, + "content": "of a canonical solution (checks condition and adds to ret.), with the anchor function (for var loop).", + "type": "text" + } + ], + "index": 8 + } + ], + "index": 5.5 + } + ], + "index": 3.25 + }, + { + "type": "text", + "bbox": [ + 106, + 219, + 505, + 275 + ], + "lines": [], + "index": 11, + "bbox_fs": [ + 104, + 221, + 506, + 277 + ], + "lines_deleted": true + }, + { + "type": "text", + "bbox": [ + 106, + 280, + 505, + 357 + ], + "lines": [ + { + "bbox": [ + 105, + 279, + 505, + 294 + ], + "spans": [ + { + "bbox": [ + 105, + 279, + 505, + 294 + ], + "score": 1.0, + "content": "We report the results of our framing experiments in Table 1. We find that adding irrelevant preceding", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 291, + 506, + 304 + ], + "spans": [ + { + "bbox": [ + 106, + 291, + 506, + 304 + ], + "score": 1.0, + "content": "functions consistently lowers functional accuracy, by between 22.3 and 30.5 points for Codex, across", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 106, + 302, + 506, + 315 + ], + "spans": [ + { + "bbox": [ + 106, + 302, + 506, + 315 + ], + "score": 1.0, + "content": "the different framing lines we tested. Moreover, both models frequently generate the framing line:", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 106, + 313, + 505, + 326 + ], + "spans": [ + { + "bbox": [ + 106, + 313, + 127, + 324 + ], + "score": 0.86, + "content": "81 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 127, + 313, + 240, + 326 + ], + "score": 1.0, + "content": "of the time for Codex and", + "type": "text" + }, + { + "bbox": [ + 241, + 313, + 268, + 324 + ], + "score": 0.88, + "content": "7 0 . 7 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 269, + 313, + 439, + 326 + ], + "score": 1.0, + "content": "of time for CodeGen, compared to only", + "type": "text" + }, + { + "bbox": [ + 439, + 313, + 462, + 324 + ], + "score": 0.87, + "content": "4 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 462, + 313, + 482, + 326 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 482, + 313, + 505, + 324 + ], + "score": 0.85, + "content": "0 . 0 \\%", + "type": "inline_equation" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 324, + 505, + 337 + ], + "spans": [ + { + "bbox": [ + 106, + 324, + 505, + 337 + ], + "score": 1.0, + "content": "over untransformed prompts respectively. These results suggest that code generation models can", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 106, + 336, + 505, + 347 + ], + "spans": [ + { + "bbox": [ + 106, + 336, + 505, + 347 + ], + "score": 1.0, + "content": "erroneously rely on irrelevant information in the prompt in predictable ways, even in the extreme", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 346, + 497, + 358 + ], + "spans": [ + { + "bbox": [ + 106, + 346, + 497, + 358 + ], + "score": 1.0, + "content": "case when doing so contradicts the type specification in the function signature (return False ).", + "type": "text" + } + ], + "index": 20 + } + ], + "index": 17, + "bbox_fs": [ + 105, + 279, + 506, + 358 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 370, + 238, + 382 + ], + "lines": [ + { + "bbox": [ + 105, + 367, + 239, + 386 + ], + "spans": [ + { + "bbox": [ + 105, + 367, + 239, + 386 + ], + "score": 1.0, + "content": "3.3.2 Inspiration: Anchoring", + "type": "text" + } + ], + "index": 21 + } + ], + "index": 21 + }, + { + "type": "text", + "bbox": [ + 107, + 389, + 505, + 445 + ], + "lines": [ + { + "bbox": [ + 106, + 389, + 505, + 401 + ], + "spans": [ + { + "bbox": [ + 106, + 389, + 505, + 401 + ], + "score": 1.0, + "content": "We next draw inspiration from anchoring: humans’ tendency to insufficiently adjust their estimates", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 400, + 505, + 413 + ], + "spans": [ + { + "bbox": [ + 105, + 400, + 368, + 413 + ], + "score": 1.0, + "content": "away from initial values. For example, Tversky and Kahneman", + "type": "text" + }, + { + "bbox": [ + 368, + 400, + 396, + 412 + ], + "score": 0.57, + "content": "\\mathbb { \\underline { { \\lVert \\mathbf { 9 7 4 } } \\rVert } }", + "type": "inline_equation" + }, + { + "bbox": [ + 396, + 400, + 505, + 413 + ], + "score": 1.0, + "content": "find that subjects’ median", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 411, + 505, + 423 + ], + "spans": [ + { + "bbox": [ + 105, + 411, + 377, + 423 + ], + "score": 1.0, + "content": "estimate for the fraction of African countries in the UN shifts from", + "type": "text" + }, + { + "bbox": [ + 378, + 412, + 397, + 422 + ], + "score": 0.83, + "content": "2 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 398, + 411, + 408, + 423 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 408, + 412, + 428, + 422 + ], + "score": 0.87, + "content": "45 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 429, + 411, + 505, + 423 + ], + "score": 1.0, + "content": ", based on whether", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 420, + 506, + 436 + ], + "spans": [ + { + "bbox": [ + 105, + 420, + 345, + 436 + ], + "score": 1.0, + "content": "they were first asked if the fraction was greater or less than", + "type": "text" + }, + { + "bbox": [ + 345, + 423, + 365, + 433 + ], + "score": 0.88, + "content": "10 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 365, + 420, + 383, + 436 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 383, + 423, + 403, + 433 + ], + "score": 0.87, + "content": "65 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 403, + 420, + 506, + 436 + ], + "score": 1.0, + "content": ", respectively. Anchoring", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 433, + 497, + 446 + ], + "spans": [ + { + "bbox": [ + 105, + 433, + 497, + 446 + ], + "score": 1.0, + "content": "captures how humans adjust to partial information, versus irrelevant information (framing effect).", + "type": "text" + } + ], + "index": 26 + } + ], + "index": 24, + "bbox_fs": [ + 105, + 389, + 506, + 446 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 450, + 505, + 516 + ], + "lines": [ + { + "bbox": [ + 106, + 449, + 505, + 462 + ], + "spans": [ + { + "bbox": [ + 106, + 449, + 505, + 462 + ], + "score": 1.0, + "content": "Using anchoring as inspiration, we hypothesize that code generation models may adjust their output", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 106, + 460, + 505, + 473 + ], + "spans": [ + { + "bbox": [ + 106, + 460, + 505, + 473 + ], + "score": 1.0, + "content": "towards related solutions, when these solutions are included in the prompt. To elicit this failure, we", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 104, + 471, + 506, + 484 + ], + "spans": [ + { + "bbox": [ + 104, + 471, + 506, + 484 + ], + "score": 1.0, + "content": "prepend anchor functions to prompts: functions that are similar to a valid solution for a HumanEval", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 482, + 506, + 495 + ], + "spans": [ + { + "bbox": [ + 105, + 482, + 506, + 495 + ], + "score": 1.0, + "content": "prompt, but contain some error. We first check that prepending these anchor functions decreases", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 493, + 505, + 506 + ], + "spans": [ + { + "bbox": [ + 105, + 493, + 243, + 506 + ], + "score": 1.0, + "content": "functional accuracy, as in Section", + "type": "text" + }, + { + "bbox": [ + 244, + 493, + 269, + 506 + ], + "score": 0.48, + "content": "3 . 3 . 1 .", + "type": "inline_equation" + }, + { + "bbox": [ + 270, + 493, + 505, + 506 + ], + "score": 1.0, + "content": "Next, to test if models adjust their output towards related", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 505, + 462, + 516 + ], + "spans": [ + { + "bbox": [ + 106, + 505, + 462, + 516 + ], + "score": 1.0, + "content": "solutions, we check that the generated solution contains elements of the anchor function.", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 29.5, + "bbox_fs": [ + 104, + 449, + 506, + 516 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 520, + 505, + 565 + ], + "lines": [ + { + "bbox": [ + 106, + 521, + 504, + 532 + ], + "spans": [ + { + "bbox": [ + 106, + 521, + 504, + 532 + ], + "score": 1.0, + "content": "We aim to construct anchor functions that are similar to functions in HumanEval prompts and that", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 532, + 505, + 543 + ], + "spans": [ + { + "bbox": [ + 105, + 532, + 505, + 543 + ], + "score": 1.0, + "content": "compile, but are incorrect. To do so, we take a prefix of the canonical solution, then add additional", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 542, + 505, + 555 + ], + "spans": [ + { + "bbox": [ + 105, + 543, + 331, + 555 + ], + "score": 1.0, + "content": "anchor lines that produce an incorrect output. See Figure", + "type": "text" + }, + { + "bbox": [ + 331, + 542, + 340, + 555 + ], + "score": 0.56, + "content": "\\triangledown", + "type": "inline_equation" + }, + { + "bbox": [ + 341, + 543, + 505, + 555 + ], + "score": 1.0, + "content": "for an example. We describe two types of", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 552, + 507, + 567 + ], + "spans": [ + { + "bbox": [ + 105, + 552, + 507, + 567 + ], + "score": 1.0, + "content": "anchor lines, and how we test their influence on the generated solutions, in the following paragraphs.", + "type": "text" + } + ], + "index": 36 + } + ], + "index": 34.5, + "bbox_fs": [ + 105, + 521, + 507, + 567 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 570, + 505, + 603 + ], + "lines": [ + { + "bbox": [ + 106, + 570, + 505, + 582 + ], + "spans": [ + { + "bbox": [ + 106, + 570, + 505, + 582 + ], + "score": 1.0, + "content": "Print-var anchor lines. We first study print-var anchor lines, which iterate over all variables in the", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 581, + 505, + 592 + ], + "spans": [ + { + "bbox": [ + 106, + 581, + 505, + 592 + ], + "score": 1.0, + "content": "function signature and print their values. For a function with inputs var1 and var2 , the associated", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 592, + 212, + 603 + ], + "spans": [ + { + "bbox": [ + 105, + 592, + 212, + 603 + ], + "score": 1.0, + "content": "print-var anchor lines are:", + "type": "text" + } + ], + "index": 39 + } + ], + "index": 38, + "bbox_fs": [ + 105, + 570, + 505, + 603 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 608, + 232, + 631 + ], + "lines": [ + { + "bbox": [ + 106, + 608, + 233, + 620 + ], + "spans": [ + { + "bbox": [ + 106, + 608, + 233, + 620 + ], + "score": 1.0, + "content": "for var in [var1, var2]:", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 126, + 620, + 181, + 631 + ], + "spans": [ + { + "bbox": [ + 126, + 620, + 181, + 631 + ], + "score": 1.0, + "content": "print(var)", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 40.5, + "bbox_fs": [ + 106, + 608, + 233, + 631 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 636, + 504, + 659 + ], + "lines": [ + { + "bbox": [ + 106, + 636, + 505, + 649 + ], + "spans": [ + { + "bbox": [ + 106, + 636, + 505, + 649 + ], + "score": 1.0, + "content": "To study the influence of the print-var anchor lines on the solution, we measure how often (i) just the", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 646, + 497, + 660 + ], + "spans": [ + { + "bbox": [ + 105, + 646, + 497, + 660 + ], + "score": 1.0, + "content": "first line (for loop), and (ii) just the second line (print statement) appear in the generated solution.", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 42.5, + "bbox_fs": [ + 105, + 636, + 505, + 660 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 663, + 505, + 696 + ], + "lines": [ + { + "bbox": [ + 105, + 663, + 505, + 675 + ], + "spans": [ + { + "bbox": [ + 105, + 663, + 505, + 675 + ], + "score": 1.0, + "content": "Add-var anchor lines. We also study add-var anchor lines, which return the sum of all variables in", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 675, + 505, + 686 + ], + "spans": [ + { + "bbox": [ + 106, + 675, + 505, + 686 + ], + "score": 1.0, + "content": "the function signature (converted to strings). For a function with inputs var1 and var2 , the add-var", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 684, + 176, + 698 + ], + "spans": [ + { + "bbox": [ + 105, + 684, + 176, + 698 + ], + "score": 1.0, + "content": "anchor lines are:", + "type": "text" + } + ], + "index": 46 + } + ], + "index": 45, + "bbox_fs": [ + 105, + 663, + 505, + 698 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "image", + "bbox": [ + 110, + 72, + 499, + 248 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 110, + 72, + 499, + 248 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 110, + 72, + 499, + 248 + ], + "spans": [ + { + "bbox": [ + 110, + 72, + 499, + 248 + ], + "score": 0.972, + "type": "image", + "image_path": "5fd2c0df7eb36902206a8d073de7ef1e8a2e8431015b1607d9f4d14ef1f86fae.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 110, + 72, + 499, + 130.66666666666666 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 110, + 130.66666666666666, + 499, + 189.33333333333331 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 110, + 189.33333333333331, + 499, + 247.99999999999997 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 256, + 506, + 344 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 257, + 506, + 268 + ], + "spans": [ + { + "bbox": [ + 106, + 257, + 506, + 268 + ], + "score": 1.0, + "content": "Figure 4: Results of the print-var anchoring experiment. Left. We measure the functional accuracy of", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 106, + 268, + 507, + 280 + ], + "spans": [ + { + "bbox": [ + 106, + 268, + 507, + 280 + ], + "score": 1.0, + "content": "Codex (top) and CodeGen (bottom) with no anchor function prepended (baseline acc) and with a print-", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 279, + 505, + 290 + ], + "spans": [ + { + "bbox": [ + 106, + 279, + 505, + 290 + ], + "score": 1.0, + "content": "var anchor function prepended (anchor acc), and find that prepending the anchor function consistently", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 289, + 505, + 302 + ], + "spans": [ + { + "bbox": [ + 105, + 289, + 505, + 302 + ], + "score": 1.0, + "content": "lowers accuracy. Right. We measure the influence of the anchor function on the generated solution", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 300, + 506, + 313 + ], + "spans": [ + { + "bbox": [ + 105, + 300, + 506, + 313 + ], + "score": 1.0, + "content": "by plotting the fraction of generated solutions that contain “for var in ” from the print-var anchor", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 311, + 506, + 324 + ], + "spans": [ + { + "bbox": [ + 105, + 311, + 506, + 324 + ], + "score": 1.0, + "content": "prompt (for var loop), the fraction of generated solutions that include “print(var) ” (prints var),", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 322, + 506, + 334 + ], + "spans": [ + { + "bbox": [ + 105, + 322, + 506, + 334 + ], + "score": 1.0, + "content": "and the fraction of generated solutions that output the anchor function verbatim without additional", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 333, + 497, + 346 + ], + "spans": [ + { + "bbox": [ + 106, + 333, + 497, + 346 + ], + "score": 1.0, + "content": "content (exact copy), as a function of the number of canonical solution lines added to the prompt.", + "type": "text" + } + ], + "index": 10 + } + ], + "index": 6.5 + } + ], + "index": 3.75 + }, + { + "type": "text", + "bbox": [ + 108, + 365, + 248, + 387 + ], + "lines": [ + { + "bbox": [ + 106, + 364, + 249, + 377 + ], + "spans": [ + { + "bbox": [ + 106, + 364, + 189, + 377 + ], + "score": 1.0, + "content": "tmp = str(var1)", + "type": "text" + }, + { + "bbox": [ + 189, + 366, + 198, + 375 + ], + "score": 0.33, + "content": "^ +", + "type": "inline_equation" + }, + { + "bbox": [ + 198, + 364, + 249, + 377 + ], + "score": 1.0, + "content": "str(var2)", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 375, + 162, + 389 + ], + "spans": [ + { + "bbox": [ + 105, + 375, + 162, + 389 + ], + "score": 1.0, + "content": "return tmp", + "type": "text" + } + ], + "index": 12 + } + ], + "index": 11.5 + }, + { + "type": "text", + "bbox": [ + 108, + 392, + 504, + 414 + ], + "lines": [ + { + "bbox": [ + 106, + 392, + 505, + 405 + ], + "spans": [ + { + "bbox": [ + 106, + 392, + 505, + 405 + ], + "score": 1.0, + "content": "To study the influence of the add-var anchor lines on the solution, we measure how often return", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 404, + 260, + 415 + ], + "spans": [ + { + "bbox": [ + 106, + 404, + 260, + 415 + ], + "score": 1.0, + "content": "tmp appears in the generated solution.", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 13.5 + }, + { + "type": "text", + "bbox": [ + 107, + 419, + 505, + 463 + ], + "lines": [ + { + "bbox": [ + 104, + 418, + 506, + 433 + ], + "spans": [ + { + "bbox": [ + 104, + 418, + 506, + 433 + ], + "score": 1.0, + "content": "Print-var results. In Figure 4, we show that prepending print-var anchor functions consistently lowers", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 430, + 505, + 443 + ], + "spans": [ + { + "bbox": [ + 105, + 430, + 505, + 443 + ], + "score": 1.0, + "content": "Codex and CodeGens’ functional accuracies across different number of prompted canonical solution", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 441, + 505, + 454 + ], + "spans": [ + { + "bbox": [ + 105, + 441, + 505, + 454 + ], + "score": 1.0, + "content": "lines. We vary the number of canonical solution lines to study prompts of different difficulties; as the", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 451, + 495, + 464 + ], + "spans": [ + { + "bbox": [ + 105, + 451, + 495, + 464 + ], + "score": 1.0, + "content": "number of solution lines increases, the number remaining lines models must produce decreases.4", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 16.5 + }, + { + "type": "text", + "bbox": [ + 106, + 469, + 505, + 546 + ], + "lines": [ + { + "bbox": [ + 105, + 468, + 505, + 482 + ], + "spans": [ + { + "bbox": [ + 105, + 468, + 505, + 482 + ], + "score": 1.0, + "content": "We additionally find that elements of anchor function often appear in both models’ outputs, suggesting", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 479, + 505, + 492 + ], + "spans": [ + { + "bbox": [ + 106, + 480, + 447, + 492 + ], + "score": 1.0, + "content": "that code generation models adjust their solutions towards related solutions. In Figure", + "type": "text" + }, + { + "bbox": [ + 447, + 479, + 457, + 492 + ], + "score": 0.51, + "content": "^ { 4 , }", + "type": "inline_equation" + }, + { + "bbox": [ + 458, + 480, + 505, + 492 + ], + "score": 1.0, + "content": "we see that", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 106, + 491, + 506, + 502 + ], + "spans": [ + { + "bbox": [ + 106, + 491, + 222, + 502 + ], + "score": 1.0, + "content": "Codex generates for var in", + "type": "text" + }, + { + "bbox": [ + 222, + 491, + 264, + 501 + ], + "score": 0.88, + "content": "3 2 \\% { - } 6 1 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 264, + 491, + 506, + 502 + ], + "score": 1.0, + "content": "of solutions when at least one line of the canonical solution is", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 501, + 505, + 514 + ], + "spans": [ + { + "bbox": [ + 105, + 501, + 266, + 514 + ], + "score": 1.0, + "content": "included, and generates print(var) in", + "type": "text" + }, + { + "bbox": [ + 267, + 502, + 309, + 512 + ], + "score": 0.89, + "content": "2 6 \\% - 4 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 309, + 501, + 505, + 514 + ], + "score": 1.0, + "content": "of solutions. CodeGen’s behavior is qualitatively", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 511, + 506, + 525 + ], + "spans": [ + { + "bbox": [ + 105, + 511, + 506, + 525 + ], + "score": 1.0, + "content": "similar. Both models sometimes even incorporate the anchor lines into correct solutions; on Codex,", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 523, + 506, + 536 + ], + "spans": [ + { + "bbox": [ + 106, + 523, + 302, + 536 + ], + "score": 1.0, + "content": "the for var loop is used in a correct solution for", + "type": "text" + }, + { + "bbox": [ + 303, + 523, + 340, + 534 + ], + "score": 0.87, + "content": "3 \\% - 1 1 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 340, + 523, + 506, + 536 + ], + "score": 1.0, + "content": "of all outputs, while print(var) is used", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 534, + 281, + 547 + ], + "spans": [ + { + "bbox": [ + 105, + 534, + 203, + 547 + ], + "score": 1.0, + "content": "in a correct solution for", + "type": "text" + }, + { + "bbox": [ + 203, + 534, + 235, + 545 + ], + "score": 0.9, + "content": "1 \\% - 9 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 236, + 534, + 281, + 547 + ], + "score": 1.0, + "content": "of outputs.", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 22 + }, + { + "type": "text", + "bbox": [ + 107, + 550, + 505, + 595 + ], + "lines": [ + { + "bbox": [ + 106, + 550, + 506, + 563 + ], + "spans": [ + { + "bbox": [ + 106, + 550, + 506, + 563 + ], + "score": 1.0, + "content": "Control experiments. One concern might be that models just outputs the anchor function verbatim,", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 561, + 506, + 574 + ], + "spans": [ + { + "bbox": [ + 106, + 562, + 157, + 573 + ], + "score": 1.0, + "content": "as in Section", + "type": "text" + }, + { + "bbox": [ + 158, + 561, + 182, + 574 + ], + "score": 0.87, + "content": "\\underline { { \\left. 3 . 3 . 1 \\right. } }", + "type": "inline_equation" + }, + { + "bbox": [ + 182, + 562, + 506, + 573 + ], + "score": 1.0, + "content": "but we find that this does not explain the full results—both models include anchor", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 572, + 505, + 585 + ], + "spans": [ + { + "bbox": [ + 105, + 572, + 505, + 585 + ], + "score": 1.0, + "content": "lines in many solutions that do not copy the anchor function verbatim. We also find that changing the", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 583, + 474, + 596 + ], + "spans": [ + { + "bbox": [ + 105, + 583, + 474, + 596 + ], + "score": 1.0, + "content": "name of the anchor function leads to only negligible changes; see Appendix A.1 for details.", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 27.5 + }, + { + "type": "text", + "bbox": [ + 107, + 600, + 504, + 623 + ], + "lines": [ + { + "bbox": [ + 106, + 600, + 505, + 611 + ], + "spans": [ + { + "bbox": [ + 106, + 600, + 505, + 611 + ], + "score": 1.0, + "content": "Add-var results. We next consider results for add-var anchor lines. Full results for the add-var", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 610, + 502, + 623 + ], + "spans": [ + { + "bbox": [ + 105, + 610, + 277, + 623 + ], + "score": 1.0, + "content": "anchor prompts are presented in Appendix", + "type": "text" + }, + { + "bbox": [ + 277, + 610, + 296, + 623 + ], + "score": 0.48, + "content": "\\mathbf { A . l }", + "type": "inline_equation" + }, + { + "bbox": [ + 297, + 610, + 502, + 623 + ], + "score": 1.0, + "content": "and are qualitatively similar to the print-var results.", + "type": "text" + } + ], + "index": 31 + } + ], + "index": 30.5 + }, + { + "type": "text", + "bbox": [ + 107, + 627, + 505, + 704 + ], + "lines": [ + { + "bbox": [ + 105, + 626, + 507, + 640 + ], + "spans": [ + { + "bbox": [ + 105, + 626, + 507, + 640 + ], + "score": 1.0, + "content": "One again, we find that prepending the anchor function consistently lowers functional accuracy.", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 106, + 638, + 504, + 649 + ], + "spans": [ + { + "bbox": [ + 106, + 638, + 504, + 649 + ], + "score": 1.0, + "content": "Moreover, the outputted solutions often include an anchor line. For example, Codex and CodeGen", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 104, + 648, + 506, + 663 + ], + "spans": [ + { + "bbox": [ + 104, + 648, + 206, + 663 + ], + "score": 1.0, + "content": "generate return tmp in", + "type": "text" + }, + { + "bbox": [ + 207, + 649, + 248, + 660 + ], + "score": 0.89, + "content": "2 6 \\% - 4 6 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 249, + 648, + 266, + 663 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 266, + 649, + 308, + 659 + ], + "score": 0.9, + "content": "1 3 \\% - 7 9 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 308, + 648, + 506, + 663 + ], + "score": 1.0, + "content": "of solutions respectively, depending on how many", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 106, + 660, + 505, + 672 + ], + "spans": [ + { + "bbox": [ + 106, + 660, + 505, + 672 + ], + "score": 1.0, + "content": "canonical solution lines we prompt with. These results are not caused by models outputting the", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 671, + 505, + 683 + ], + "spans": [ + { + "bbox": [ + 105, + 671, + 327, + 683 + ], + "score": 1.0, + "content": "anchoring function verbatim: this only occurs between", + "type": "text" + }, + { + "bbox": [ + 328, + 671, + 343, + 681 + ], + "score": 0.85, + "content": "7 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 343, + 671, + 361, + 683 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 361, + 671, + 381, + 681 + ], + "score": 0.87, + "content": "12 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 381, + 671, + 489, + 683 + ], + "score": 1.0, + "content": "of the time for Codex, and", + "type": "text" + }, + { + "bbox": [ + 490, + 671, + 505, + 681 + ], + "score": 0.84, + "content": "4 \\%", + "type": "inline_equation" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 680, + 506, + 695 + ], + "spans": [ + { + "bbox": [ + 105, + 680, + 123, + 695 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 123, + 682, + 142, + 692 + ], + "score": 0.87, + "content": "12 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 142, + 680, + 506, + 695 + ], + "score": 1.0, + "content": "for CodeGen. Overall, our findings suggest that code generation models can err by adjusting", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 692, + 438, + 705 + ], + "spans": [ + { + "bbox": [ + 105, + 692, + 438, + 705 + ], + "score": 1.0, + "content": "its output towards related solutions, when the solutions are included in the prompt.", + "type": "text" + } + ], + "index": 38 + } + ], + "index": 35 + } + ], + "page_idx": 5, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 114, + 711, + 491, + 722 + ], + "lines": [ + { + "bbox": [ + 118, + 708, + 493, + 725 + ], + "spans": [ + { + "bbox": [ + 118, + 708, + 493, + 725 + ], + "score": 1.0, + "content": "4 We filter out programs whose entire canonical solution would appear in the prompt; see Appendix A.1.", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 302, + 742, + 309, + 750 + ], + "lines": [ + { + "bbox": [ + 302, + 741, + 310, + 752 + ], + "spans": [ + { + "bbox": [ + 302, + 741, + 310, + 752 + ], + "score": 1.0, + "content": "6", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 333, + 74, + 490, + 81 + ], + "lines": [] + } + ], + "para_blocks": [ + { + "type": "image", + "bbox": [ + 110, + 72, + 499, + 248 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 110, + 72, + 499, + 248 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 110, + 72, + 499, + 248 + ], + "spans": [ + { + "bbox": [ + 110, + 72, + 499, + 248 + ], + "score": 0.972, + "type": "image", + "image_path": "5fd2c0df7eb36902206a8d073de7ef1e8a2e8431015b1607d9f4d14ef1f86fae.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 110, + 72, + 499, + 130.66666666666666 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 110, + 130.66666666666666, + 499, + 189.33333333333331 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 110, + 189.33333333333331, + 499, + 247.99999999999997 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 256, + 506, + 344 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 257, + 506, + 268 + ], + "spans": [ + { + "bbox": [ + 106, + 257, + 506, + 268 + ], + "score": 1.0, + "content": "Figure 4: Results of the print-var anchoring experiment. Left. We measure the functional accuracy of", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 106, + 268, + 507, + 280 + ], + "spans": [ + { + "bbox": [ + 106, + 268, + 507, + 280 + ], + "score": 1.0, + "content": "Codex (top) and CodeGen (bottom) with no anchor function prepended (baseline acc) and with a print-", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 279, + 505, + 290 + ], + "spans": [ + { + "bbox": [ + 106, + 279, + 505, + 290 + ], + "score": 1.0, + "content": "var anchor function prepended (anchor acc), and find that prepending the anchor function consistently", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 289, + 505, + 302 + ], + "spans": [ + { + "bbox": [ + 105, + 289, + 505, + 302 + ], + "score": 1.0, + "content": "lowers accuracy. Right. We measure the influence of the anchor function on the generated solution", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 300, + 506, + 313 + ], + "spans": [ + { + "bbox": [ + 105, + 300, + 506, + 313 + ], + "score": 1.0, + "content": "by plotting the fraction of generated solutions that contain “for var in ” from the print-var anchor", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 311, + 506, + 324 + ], + "spans": [ + { + "bbox": [ + 105, + 311, + 506, + 324 + ], + "score": 1.0, + "content": "prompt (for var loop), the fraction of generated solutions that include “print(var) ” (prints var),", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 322, + 506, + 334 + ], + "spans": [ + { + "bbox": [ + 105, + 322, + 506, + 334 + ], + "score": 1.0, + "content": "and the fraction of generated solutions that output the anchor function verbatim without additional", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 333, + 497, + 346 + ], + "spans": [ + { + "bbox": [ + 106, + 333, + 497, + 346 + ], + "score": 1.0, + "content": "content (exact copy), as a function of the number of canonical solution lines added to the prompt.", + "type": "text" + } + ], + "index": 10 + } + ], + "index": 6.5 + } + ], + "index": 3.75 + }, + { + "type": "text", + "bbox": [ + 108, + 365, + 248, + 387 + ], + "lines": [ + { + "bbox": [ + 106, + 364, + 249, + 377 + ], + "spans": [ + { + "bbox": [ + 106, + 364, + 189, + 377 + ], + "score": 1.0, + "content": "tmp = str(var1)", + "type": "text" + }, + { + "bbox": [ + 189, + 366, + 198, + 375 + ], + "score": 0.33, + "content": "^ +", + "type": "inline_equation" + }, + { + "bbox": [ + 198, + 364, + 249, + 377 + ], + "score": 1.0, + "content": "str(var2)", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 375, + 162, + 389 + ], + "spans": [ + { + "bbox": [ + 105, + 375, + 162, + 389 + ], + "score": 1.0, + "content": "return tmp", + "type": "text" + } + ], + "index": 12 + } + ], + "index": 11.5, + "bbox_fs": [ + 105, + 364, + 249, + 389 + ] + }, + { + "type": "text", + "bbox": [ + 108, + 392, + 504, + 414 + ], + "lines": [ + { + "bbox": [ + 106, + 392, + 505, + 405 + ], + "spans": [ + { + "bbox": [ + 106, + 392, + 505, + 405 + ], + "score": 1.0, + "content": "To study the influence of the add-var anchor lines on the solution, we measure how often return", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 404, + 260, + 415 + ], + "spans": [ + { + "bbox": [ + 106, + 404, + 260, + 415 + ], + "score": 1.0, + "content": "tmp appears in the generated solution.", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 13.5, + "bbox_fs": [ + 106, + 392, + 505, + 415 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 419, + 505, + 463 + ], + "lines": [ + { + "bbox": [ + 104, + 418, + 506, + 433 + ], + "spans": [ + { + "bbox": [ + 104, + 418, + 506, + 433 + ], + "score": 1.0, + "content": "Print-var results. In Figure 4, we show that prepending print-var anchor functions consistently lowers", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 430, + 505, + 443 + ], + "spans": [ + { + "bbox": [ + 105, + 430, + 505, + 443 + ], + "score": 1.0, + "content": "Codex and CodeGens’ functional accuracies across different number of prompted canonical solution", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 441, + 505, + 454 + ], + "spans": [ + { + "bbox": [ + 105, + 441, + 505, + 454 + ], + "score": 1.0, + "content": "lines. We vary the number of canonical solution lines to study prompts of different difficulties; as the", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 451, + 495, + 464 + ], + "spans": [ + { + "bbox": [ + 105, + 451, + 495, + 464 + ], + "score": 1.0, + "content": "number of solution lines increases, the number remaining lines models must produce decreases.4", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 16.5, + "bbox_fs": [ + 104, + 418, + 506, + 464 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 469, + 505, + 546 + ], + "lines": [ + { + "bbox": [ + 105, + 468, + 505, + 482 + ], + "spans": [ + { + "bbox": [ + 105, + 468, + 505, + 482 + ], + "score": 1.0, + "content": "We additionally find that elements of anchor function often appear in both models’ outputs, suggesting", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 479, + 505, + 492 + ], + "spans": [ + { + "bbox": [ + 106, + 480, + 447, + 492 + ], + "score": 1.0, + "content": "that code generation models adjust their solutions towards related solutions. In Figure", + "type": "text" + }, + { + "bbox": [ + 447, + 479, + 457, + 492 + ], + "score": 0.51, + "content": "^ { 4 , }", + "type": "inline_equation" + }, + { + "bbox": [ + 458, + 480, + 505, + 492 + ], + "score": 1.0, + "content": "we see that", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 106, + 491, + 506, + 502 + ], + "spans": [ + { + "bbox": [ + 106, + 491, + 222, + 502 + ], + "score": 1.0, + "content": "Codex generates for var in", + "type": "text" + }, + { + "bbox": [ + 222, + 491, + 264, + 501 + ], + "score": 0.88, + "content": "3 2 \\% { - } 6 1 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 264, + 491, + 506, + 502 + ], + "score": 1.0, + "content": "of solutions when at least one line of the canonical solution is", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 501, + 505, + 514 + ], + "spans": [ + { + "bbox": [ + 105, + 501, + 266, + 514 + ], + "score": 1.0, + "content": "included, and generates print(var) in", + "type": "text" + }, + { + "bbox": [ + 267, + 502, + 309, + 512 + ], + "score": 0.89, + "content": "2 6 \\% - 4 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 309, + 501, + 505, + 514 + ], + "score": 1.0, + "content": "of solutions. CodeGen’s behavior is qualitatively", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 511, + 506, + 525 + ], + "spans": [ + { + "bbox": [ + 105, + 511, + 506, + 525 + ], + "score": 1.0, + "content": "similar. Both models sometimes even incorporate the anchor lines into correct solutions; on Codex,", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 523, + 506, + 536 + ], + "spans": [ + { + "bbox": [ + 106, + 523, + 302, + 536 + ], + "score": 1.0, + "content": "the for var loop is used in a correct solution for", + "type": "text" + }, + { + "bbox": [ + 303, + 523, + 340, + 534 + ], + "score": 0.87, + "content": "3 \\% - 1 1 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 340, + 523, + 506, + 536 + ], + "score": 1.0, + "content": "of all outputs, while print(var) is used", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 534, + 281, + 547 + ], + "spans": [ + { + "bbox": [ + 105, + 534, + 203, + 547 + ], + "score": 1.0, + "content": "in a correct solution for", + "type": "text" + }, + { + "bbox": [ + 203, + 534, + 235, + 545 + ], + "score": 0.9, + "content": "1 \\% - 9 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 236, + 534, + 281, + 547 + ], + "score": 1.0, + "content": "of outputs.", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 22, + "bbox_fs": [ + 105, + 468, + 506, + 547 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 550, + 505, + 595 + ], + "lines": [ + { + "bbox": [ + 106, + 550, + 506, + 563 + ], + "spans": [ + { + "bbox": [ + 106, + 550, + 506, + 563 + ], + "score": 1.0, + "content": "Control experiments. One concern might be that models just outputs the anchor function verbatim,", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 561, + 506, + 574 + ], + "spans": [ + { + "bbox": [ + 106, + 562, + 157, + 573 + ], + "score": 1.0, + "content": "as in Section", + "type": "text" + }, + { + "bbox": [ + 158, + 561, + 182, + 574 + ], + "score": 0.87, + "content": "\\underline { { \\left. 3 . 3 . 1 \\right. } }", + "type": "inline_equation" + }, + { + "bbox": [ + 182, + 562, + 506, + 573 + ], + "score": 1.0, + "content": "but we find that this does not explain the full results—both models include anchor", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 572, + 505, + 585 + ], + "spans": [ + { + "bbox": [ + 105, + 572, + 505, + 585 + ], + "score": 1.0, + "content": "lines in many solutions that do not copy the anchor function verbatim. We also find that changing the", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 583, + 474, + 596 + ], + "spans": [ + { + "bbox": [ + 105, + 583, + 474, + 596 + ], + "score": 1.0, + "content": "name of the anchor function leads to only negligible changes; see Appendix A.1 for details.", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 27.5, + "bbox_fs": [ + 105, + 550, + 506, + 596 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 600, + 504, + 623 + ], + "lines": [ + { + "bbox": [ + 106, + 600, + 505, + 611 + ], + "spans": [ + { + "bbox": [ + 106, + 600, + 505, + 611 + ], + "score": 1.0, + "content": "Add-var results. We next consider results for add-var anchor lines. Full results for the add-var", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 610, + 502, + 623 + ], + "spans": [ + { + "bbox": [ + 105, + 610, + 277, + 623 + ], + "score": 1.0, + "content": "anchor prompts are presented in Appendix", + "type": "text" + }, + { + "bbox": [ + 277, + 610, + 296, + 623 + ], + "score": 0.48, + "content": "\\mathbf { A . l }", + "type": "inline_equation" + }, + { + "bbox": [ + 297, + 610, + 502, + 623 + ], + "score": 1.0, + "content": "and are qualitatively similar to the print-var results.", + "type": "text" + } + ], + "index": 31 + } + ], + "index": 30.5, + "bbox_fs": [ + 105, + 600, + 505, + 623 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 627, + 505, + 704 + ], + "lines": [ + { + "bbox": [ + 105, + 626, + 507, + 640 + ], + "spans": [ + { + "bbox": [ + 105, + 626, + 507, + 640 + ], + "score": 1.0, + "content": "One again, we find that prepending the anchor function consistently lowers functional accuracy.", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 106, + 638, + 504, + 649 + ], + "spans": [ + { + "bbox": [ + 106, + 638, + 504, + 649 + ], + "score": 1.0, + "content": "Moreover, the outputted solutions often include an anchor line. For example, Codex and CodeGen", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 104, + 648, + 506, + 663 + ], + "spans": [ + { + "bbox": [ + 104, + 648, + 206, + 663 + ], + "score": 1.0, + "content": "generate return tmp in", + "type": "text" + }, + { + "bbox": [ + 207, + 649, + 248, + 660 + ], + "score": 0.89, + "content": "2 6 \\% - 4 6 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 249, + 648, + 266, + 663 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 266, + 649, + 308, + 659 + ], + "score": 0.9, + "content": "1 3 \\% - 7 9 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 308, + 648, + 506, + 663 + ], + "score": 1.0, + "content": "of solutions respectively, depending on how many", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 106, + 660, + 505, + 672 + ], + "spans": [ + { + "bbox": [ + 106, + 660, + 505, + 672 + ], + "score": 1.0, + "content": "canonical solution lines we prompt with. These results are not caused by models outputting the", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 671, + 505, + 683 + ], + "spans": [ + { + "bbox": [ + 105, + 671, + 327, + 683 + ], + "score": 1.0, + "content": "anchoring function verbatim: this only occurs between", + "type": "text" + }, + { + "bbox": [ + 328, + 671, + 343, + 681 + ], + "score": 0.85, + "content": "7 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 343, + 671, + 361, + 683 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 361, + 671, + 381, + 681 + ], + "score": 0.87, + "content": "12 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 381, + 671, + 489, + 683 + ], + "score": 1.0, + "content": "of the time for Codex, and", + "type": "text" + }, + { + "bbox": [ + 490, + 671, + 505, + 681 + ], + "score": 0.84, + "content": "4 \\%", + "type": "inline_equation" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 680, + 506, + 695 + ], + "spans": [ + { + "bbox": [ + 105, + 680, + 123, + 695 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 123, + 682, + 142, + 692 + ], + "score": 0.87, + "content": "12 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 142, + 680, + 506, + 695 + ], + "score": 1.0, + "content": "for CodeGen. Overall, our findings suggest that code generation models can err by adjusting", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 692, + 438, + 705 + ], + "spans": [ + { + "bbox": [ + 105, + 692, + 438, + 705 + ], + "score": 1.0, + "content": "its output towards related solutions, when the solutions are included in the prompt.", + "type": "text" + } + ], + "index": 38 + } + ], + "index": 35, + "bbox_fs": [ + 104, + 626, + 507, + 705 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "text", + "bbox": [ + 113, + 83, + 286, + 115 + ], + "lines": [ + { + "bbox": [ + 112, + 82, + 285, + 95 + ], + "spans": [ + { + "bbox": [ + 112, + 82, + 285, + 95 + ], + "score": 1.0, + "content": "Write a function that squares", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 113, + 95, + 239, + 106 + ], + "spans": [ + { + "bbox": [ + 113, + 95, + 239, + 106 + ], + "score": 1.0, + "content": "the sum of its inputs", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 113, + 106, + 133, + 115 + ], + "spans": [ + { + "bbox": [ + 113, + 106, + 133, + 115 + ], + "score": 0.0, + "content": "", + "type": "text" + } + ], + "index": 2 + } + ], + "index": 1 + }, + { + "type": "text", + "bbox": [ + 310, + 84, + 489, + 115 + ], + "lines": [ + { + "bbox": [ + 309, + 82, + 488, + 95 + ], + "spans": [ + { + "bbox": [ + 309, + 82, + 488, + 95 + ], + "score": 1.0, + "content": "Write a function that sums its", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 310, + 94, + 477, + 107 + ], + "spans": [ + { + "bbox": [ + 310, + 94, + 477, + 107 + ], + "score": 1.0, + "content": "inputs called product_plus_2", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 310, + 106, + 330, + 115 + ], + "spans": [ + { + "bbox": [ + 310, + 106, + 330, + 115 + ], + "score": 0.0, + "content": "", + "type": "text" + } + ], + "index": 5 + } + ], + "index": 4 + }, + { + "type": "text", + "bbox": [ + 113, + 120, + 273, + 141 + ], + "lines": [ + { + "bbox": [ + 112, + 118, + 239, + 132 + ], + "spans": [ + { + "bbox": [ + 112, + 118, + 239, + 132 + ], + "score": 1.0, + "content": "def square_sum(x, y):", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 141, + 129, + 272, + 142 + ], + "spans": [ + { + "bbox": [ + 141, + 129, + 183, + 142 + ], + "score": 1.0, + "content": "return", + "type": "text" + }, + { + "bbox": [ + 183, + 131, + 272, + 142 + ], + "score": 0.55, + "content": "\\texttt { x } \\star \\texttt { 2 } + \\texttt { y } \\star \\texttt { 2 }", + "type": "inline_equation" + } + ], + "index": 7 + } + ], + "index": 6.5 + }, + { + "type": "text", + "bbox": [ + 106, + 148, + 506, + 214 + ], + "lines": [ + { + "bbox": [ + 106, + 147, + 506, + 160 + ], + "spans": [ + { + "bbox": [ + 106, + 147, + 506, + 160 + ], + "score": 1.0, + "content": "Figure 5: Left. Availability heuristic example where Codex mixes up the order of operations. The", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 159, + 505, + 172 + ], + "spans": [ + { + "bbox": [ + 105, + 159, + 505, + 172 + ], + "score": 1.0, + "content": "correct function signature (blue), square_sum matches the prompt. However, the incorrect function", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 169, + 506, + 183 + ], + "spans": [ + { + "bbox": [ + 105, + 169, + 506, + 183 + ], + "score": 1.0, + "content": "call (red) instead squares its inputs before summing them. The prompt is above the horizontal line,", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 180, + 506, + 193 + ], + "spans": [ + { + "bbox": [ + 105, + 180, + 506, + 193 + ], + "score": 1.0, + "content": "while the generated code is below. Right. Attribute substitution example where Codex relies on the", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 191, + 506, + 204 + ], + "spans": [ + { + "bbox": [ + 106, + 191, + 506, + 204 + ], + "score": 1.0, + "content": "function name to generate output. Codex correctly generates the desired function name (blue), but", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 203, + 474, + 216 + ], + "spans": [ + { + "bbox": [ + 106, + 203, + 474, + 216 + ], + "score": 1.0, + "content": "errs by using the function name instead of the prompt to generate the return statement (red).", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 10.5 + }, + { + "type": "title", + "bbox": [ + 108, + 234, + 281, + 246 + ], + "lines": [ + { + "bbox": [ + 105, + 234, + 281, + 248 + ], + "spans": [ + { + "bbox": [ + 105, + 234, + 281, + 248 + ], + "score": 1.0, + "content": "3.3.3 Inspiration: Availability heuristic", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 14 + }, + { + "type": "text", + "bbox": [ + 107, + 253, + 505, + 297 + ], + "lines": [ + { + "bbox": [ + 105, + 253, + 506, + 266 + ], + "spans": [ + { + "bbox": [ + 105, + 253, + 506, + 266 + ], + "score": 1.0, + "content": "We next draw inspiration from the availability heuristic: the tendency of humans to evaluate how", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 264, + 506, + 276 + ], + "spans": [ + { + "bbox": [ + 105, + 264, + 506, + 276 + ], + "score": 1.0, + "content": "frequently an example occurs based on how easy it is to recall. For example, Tversky and Kahneman", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 107, + 275, + 505, + 288 + ], + "spans": [ + { + "bbox": [ + 107, + 275, + 135, + 287 + ], + "score": 0.56, + "content": "\\mathbb { \\underline { { \\lVert \\nabla ^ { 9 } 7 3 \\rVert } } }", + "type": "inline_equation" + }, + { + "bbox": [ + 135, + 275, + 505, + 288 + ], + "score": 1.0, + "content": "find that humans tend to incorrectly report that there are more first words that start with “r”", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 286, + 446, + 299 + ], + "spans": [ + { + "bbox": [ + 105, + 286, + 446, + 299 + ], + "score": 1.0, + "content": "and “k” than have third letter “r” and “k”, because the former quickly come to mind.", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 16.5 + }, + { + "type": "text", + "bbox": [ + 107, + 302, + 505, + 390 + ], + "lines": [ + { + "bbox": [ + 105, + 302, + 505, + 316 + ], + "spans": [ + { + "bbox": [ + 105, + 302, + 505, + 316 + ], + "score": 1.0, + "content": "Using the availability heuristic as motivation, we hypothesize that code generation models may err by", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 313, + 505, + 326 + ], + "spans": [ + { + "bbox": [ + 105, + 313, + 505, + 326 + ], + "score": 1.0, + "content": "outputting solutions to related prompts that appear more frequently in the training set. To elicit this", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 324, + 505, + 338 + ], + "spans": [ + { + "bbox": [ + 105, + 324, + 505, + 338 + ], + "score": 1.0, + "content": "failure, we start with prompts that apply a unary operation before a binary operation (unary-first), then", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 335, + 505, + 349 + ], + "spans": [ + { + "bbox": [ + 105, + 335, + 505, + 349 + ], + "score": 1.0, + "content": "flip the order (binary-first). Programmers tend to apply unary operations first (e.g. when computing", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 346, + 505, + 359 + ], + "spans": [ + { + "bbox": [ + 105, + 346, + 505, + 359 + ], + "score": 1.0, + "content": "Euclidean distances or variances), so we conjecture that they appear more frequently on GitHub. We", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 357, + 505, + 370 + ], + "spans": [ + { + "bbox": [ + 105, + 357, + 505, + 370 + ], + "score": 1.0, + "content": "first check that flipping the order of operations decreases accuracy. Next, to test if code generation", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 369, + 505, + 381 + ], + "spans": [ + { + "bbox": [ + 105, + 369, + 505, + 381 + ], + "score": 1.0, + "content": "models instead outputs related prompts that occur more frequently in the training set, we measure", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 106, + 379, + 392, + 391 + ], + "spans": [ + { + "bbox": [ + 106, + 379, + 392, + 391 + ], + "score": 1.0, + "content": "whether code generation models instead output the unary-first solution.", + "type": "text" + } + ], + "index": 26 + } + ], + "index": 22.5 + }, + { + "type": "text", + "bbox": [ + 106, + 395, + 505, + 473 + ], + "lines": [ + { + "bbox": [ + 105, + 394, + 505, + 408 + ], + "spans": [ + { + "bbox": [ + 105, + 394, + 505, + 408 + ], + "score": 1.0, + "content": "We consider all 12 combinations of the binary operations sum, difference, and product, with unary", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 405, + 505, + 420 + ], + "spans": [ + { + "bbox": [ + 105, + 405, + 505, + 420 + ], + "score": 1.0, + "content": "operations square, cube, quadruple, and square root. Focusing on Codex,5 we find that accuracy", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 417, + 505, + 429 + ], + "spans": [ + { + "bbox": [ + 106, + 417, + 151, + 429 + ], + "score": 1.0, + "content": "drops from", + "type": "text" + }, + { + "bbox": [ + 152, + 417, + 171, + 428 + ], + "score": 0.87, + "content": "50 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 171, + 417, + 182, + 429 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 182, + 417, + 201, + 428 + ], + "score": 0.87, + "content": "17 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 201, + 417, + 505, + 429 + ], + "score": 1.0, + "content": "when flipping the order from unary-first to binary-first. Among combinations", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 106, + 428, + 505, + 441 + ], + "spans": [ + { + "bbox": [ + 106, + 428, + 309, + 441 + ], + "score": 1.0, + "content": "where flipping the order leads to error, we find that", + "type": "text" + }, + { + "bbox": [ + 309, + 428, + 329, + 439 + ], + "score": 0.88, + "content": "7 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 329, + 428, + 505, + 441 + ], + "score": 1.0, + "content": "of the binary-first outputs are the unary-first", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 106, + 438, + 505, + 452 + ], + "spans": [ + { + "bbox": [ + 106, + 439, + 281, + 452 + ], + "score": 1.0, + "content": "solution. We exhibit one such error in Figure", + "type": "text" + }, + { + "bbox": [ + 282, + 438, + 292, + 451 + ], + "score": 0.46, + "content": "\\boxed { 5 }", + "type": "inline_equation" + }, + { + "bbox": [ + 292, + 439, + 505, + 452 + ], + "score": 1.0, + "content": "when prompted to square the sum of its inputs, Codex", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 450, + 505, + 462 + ], + "spans": [ + { + "bbox": [ + 105, + 450, + 505, + 462 + ], + "score": 1.0, + "content": "generates the correct function name (square_sum ), but reverses the order of operations. Our results", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 460, + 497, + 474 + ], + "spans": [ + { + "bbox": [ + 105, + 460, + 497, + 474 + ], + "score": 1.0, + "content": "suggest that Codex can err by outputting solutions to related, frequent prompts in the training set.", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 30 + }, + { + "type": "text", + "bbox": [ + 107, + 477, + 505, + 522 + ], + "lines": [ + { + "bbox": [ + 106, + 477, + 506, + 489 + ], + "spans": [ + { + "bbox": [ + 106, + 477, + 506, + 489 + ], + "score": 1.0, + "content": "Control experiments. One worry is that the dip in performance is due the instructional nature of our", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 488, + 506, + 500 + ], + "spans": [ + { + "bbox": [ + 105, + 488, + 506, + 500 + ], + "score": 1.0, + "content": "prompts. We rule this out by evaluating Codex on prompts where the docstring appears beneath the", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 500, + 505, + 511 + ], + "spans": [ + { + "bbox": [ + 105, + 500, + 505, + 511 + ], + "score": 1.0, + "content": "function signature and is a definition rather than command, to more closely mimic some functions on", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 509, + 487, + 524 + ], + "spans": [ + { + "bbox": [ + 105, + 509, + 487, + 524 + ], + "score": 1.0, + "content": "GitHub. We obtain qualitatively similar results on these prompts, see Appendix A.4 for details.", + "type": "text" + } + ], + "index": 37 + } + ], + "index": 35.5 + }, + { + "type": "title", + "bbox": [ + 108, + 533, + 284, + 544 + ], + "lines": [ + { + "bbox": [ + 106, + 533, + 285, + 546 + ], + "spans": [ + { + "bbox": [ + 106, + 533, + 285, + 546 + ], + "score": 1.0, + "content": "3.3.4 Inspiration: Attribute substitution", + "type": "text" + } + ], + "index": 38 + } + ], + "index": 38 + }, + { + "type": "text", + "bbox": [ + 107, + 551, + 505, + 595 + ], + "lines": [ + { + "bbox": [ + 105, + 550, + 506, + 564 + ], + "spans": [ + { + "bbox": [ + 105, + 550, + 506, + 564 + ], + "score": 1.0, + "content": "Finally, we draw inspiration from attribute substitution: the human tendency to respond to a com-", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 106, + 562, + 506, + 574 + ], + "spans": [ + { + "bbox": [ + 106, + 562, + 506, + 574 + ], + "score": 1.0, + "content": "plicated question using a simpler, related question [Kahneman and Frederick, 2002]. For example,", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 104, + 573, + 506, + 585 + ], + "spans": [ + { + "bbox": [ + 104, + 573, + 506, + 585 + ], + "score": 1.0, + "content": "a professor when asked how likely a candidate is to be tenured, may instead respond with how", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 585, + 253, + 595 + ], + "spans": [ + { + "bbox": [ + 105, + 585, + 253, + 595 + ], + "score": 1.0, + "content": "impressive they found their job talk.", + "type": "text" + } + ], + "index": 42 + } + ], + "index": 40.5 + }, + { + "type": "text", + "bbox": [ + 107, + 600, + 505, + 666 + ], + "lines": [ + { + "bbox": [ + 105, + 600, + 505, + 613 + ], + "spans": [ + { + "bbox": [ + 105, + 600, + 505, + 613 + ], + "score": 1.0, + "content": "Using attribute substitution as inspiration, we hypothesize that Codex may use simple-but-incorrect", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 611, + 506, + 624 + ], + "spans": [ + { + "bbox": [ + 105, + 611, + 506, + 624 + ], + "score": 1.0, + "content": "heuristics to generate solutions. To elicit this failure, we add requests for conflicting function names", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 622, + 506, + 636 + ], + "spans": [ + { + "bbox": [ + 105, + 622, + 506, + 636 + ], + "score": 1.0, + "content": "to MathEquation prompts. For example, in Figure 5 we prompt Codex to write a program that sums", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 633, + 505, + 645 + ], + "spans": [ + { + "bbox": [ + 106, + 633, + 505, + 645 + ], + "score": 1.0, + "content": "its inputs called product_plus_2 . We first check that adding conflicting function names decreases", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 643, + 506, + 657 + ], + "spans": [ + { + "bbox": [ + 105, + 643, + 506, + 657 + ], + "score": 1.0, + "content": "Codex’s functional accuracy. Next, to test if Codex uses simple-but-incorrect heuristics to generate", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 106, + 655, + 419, + 667 + ], + "spans": [ + { + "bbox": [ + 106, + 655, + 419, + 667 + ], + "score": 1.0, + "content": "solutions, we check whether the generate solution matches the function name.", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 45.5 + }, + { + "type": "text", + "bbox": [ + 108, + 671, + 503, + 694 + ], + "lines": [ + { + "bbox": [ + 106, + 671, + 505, + 684 + ], + "spans": [ + { + "bbox": [ + 106, + 671, + 505, + 684 + ], + "score": 1.0, + "content": "We evaluate Codex using 90 MathEquation prompts where the desired solution and requested", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 106, + 682, + 505, + 695 + ], + "spans": [ + { + "bbox": [ + 106, + 682, + 505, + 695 + ], + "score": 1.0, + "content": "function name differ. To construct prompts, we begin with a prompt that Codex originally solves", + "type": "text" + } + ], + "index": 50 + } + ], + "index": 49.5 + } + ], + "page_idx": 6, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 108, + 701, + 504, + 723 + ], + "lines": [ + { + "bbox": [ + 118, + 699, + 506, + 714 + ], + "spans": [ + { + "bbox": [ + 118, + 699, + 506, + 714 + ], + "score": 1.0, + "content": "5 We find that CodeGen often produces nonsensical solutions on the style of prompts used in Section 3.3.3,", + "type": "text" + } + ] + }, + { + "bbox": [ + 106, + 710, + 331, + 724 + ], + "spans": [ + { + "bbox": [ + 106, + 710, + 331, + 724 + ], + "score": 1.0, + "content": "Section 3.3.4, and Section 5, so we focus primarily on Codex.", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 302, + 741, + 308, + 750 + ], + "lines": [ + { + "bbox": [ + 302, + 741, + 309, + 752 + ], + "spans": [ + { + "bbox": [ + 302, + 741, + 309, + 752 + ], + "score": 1.0, + "content": "7", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 114, + 72, + 131, + 78 + ], + "lines": [ + { + "bbox": [ + 114, + 73, + 132, + 78 + ], + "spans": [ + { + "bbox": [ + 114, + 73, + 132, + 78 + ], + "score": 0.0, + "content": "", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 311, + 72, + 329, + 78 + ], + "lines": [ + { + "bbox": [ + 311, + 73, + 329, + 78 + ], + "spans": [ + { + "bbox": [ + 311, + 73, + 329, + 78 + ], + "score": 0.0, + "content": "", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 311, + 119, + 457, + 141 + ], + "lines": [ + { + "bbox": [ + 309, + 117, + 459, + 132 + ], + "spans": [ + { + "bbox": [ + 309, + 117, + 459, + 132 + ], + "score": 1.0, + "content": "def product_plus_2(x, y):", + "type": "text" + } + ] + }, + { + "bbox": [ + 333, + 130, + 430, + 142 + ], + "spans": [ + { + "bbox": [ + 333, + 130, + 430, + 142 + ], + "score": 1.0, + "content": "return x * y + 2", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "text", + "bbox": [ + 113, + 83, + 286, + 115 + ], + "lines": [ + { + "bbox": [ + 112, + 82, + 285, + 95 + ], + "spans": [ + { + "bbox": [ + 112, + 82, + 285, + 95 + ], + "score": 1.0, + "content": "Write a function that squares", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 113, + 95, + 239, + 106 + ], + "spans": [ + { + "bbox": [ + 113, + 95, + 239, + 106 + ], + "score": 1.0, + "content": "the sum of its inputs", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 113, + 106, + 133, + 115 + ], + "spans": [ + { + "bbox": [ + 113, + 106, + 133, + 115 + ], + "score": 0.0, + "content": "", + "type": "text" + } + ], + "index": 2 + } + ], + "index": 1, + "bbox_fs": [ + 112, + 82, + 285, + 115 + ] + }, + { + "type": "text", + "bbox": [ + 310, + 84, + 489, + 115 + ], + "lines": [ + { + "bbox": [ + 309, + 82, + 488, + 95 + ], + "spans": [ + { + "bbox": [ + 309, + 82, + 488, + 95 + ], + "score": 1.0, + "content": "Write a function that sums its", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 310, + 94, + 477, + 107 + ], + "spans": [ + { + "bbox": [ + 310, + 94, + 477, + 107 + ], + "score": 1.0, + "content": "inputs called product_plus_2", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 310, + 106, + 330, + 115 + ], + "spans": [ + { + "bbox": [ + 310, + 106, + 330, + 115 + ], + "score": 0.0, + "content": "", + "type": "text" + } + ], + "index": 5 + } + ], + "index": 4, + "bbox_fs": [ + 309, + 82, + 488, + 115 + ] + }, + { + "type": "text", + "bbox": [ + 113, + 120, + 273, + 141 + ], + "lines": [ + { + "bbox": [ + 112, + 118, + 239, + 132 + ], + "spans": [ + { + "bbox": [ + 112, + 118, + 239, + 132 + ], + "score": 1.0, + "content": "def square_sum(x, y):", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 141, + 129, + 272, + 142 + ], + "spans": [ + { + "bbox": [ + 141, + 129, + 183, + 142 + ], + "score": 1.0, + "content": "return", + "type": "text" + }, + { + "bbox": [ + 183, + 131, + 272, + 142 + ], + "score": 0.55, + "content": "\\texttt { x } \\star \\texttt { 2 } + \\texttt { y } \\star \\texttt { 2 }", + "type": "inline_equation" + } + ], + "index": 7 + } + ], + "index": 6.5, + "bbox_fs": [ + 112, + 118, + 272, + 142 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 148, + 506, + 214 + ], + "lines": [ + { + "bbox": [ + 106, + 147, + 506, + 160 + ], + "spans": [ + { + "bbox": [ + 106, + 147, + 506, + 160 + ], + "score": 1.0, + "content": "Figure 5: Left. Availability heuristic example where Codex mixes up the order of operations. The", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 159, + 505, + 172 + ], + "spans": [ + { + "bbox": [ + 105, + 159, + 505, + 172 + ], + "score": 1.0, + "content": "correct function signature (blue), square_sum matches the prompt. However, the incorrect function", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 169, + 506, + 183 + ], + "spans": [ + { + "bbox": [ + 105, + 169, + 506, + 183 + ], + "score": 1.0, + "content": "call (red) instead squares its inputs before summing them. The prompt is above the horizontal line,", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 180, + 506, + 193 + ], + "spans": [ + { + "bbox": [ + 105, + 180, + 506, + 193 + ], + "score": 1.0, + "content": "while the generated code is below. Right. Attribute substitution example where Codex relies on the", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 191, + 506, + 204 + ], + "spans": [ + { + "bbox": [ + 106, + 191, + 506, + 204 + ], + "score": 1.0, + "content": "function name to generate output. Codex correctly generates the desired function name (blue), but", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 203, + 474, + 216 + ], + "spans": [ + { + "bbox": [ + 106, + 203, + 474, + 216 + ], + "score": 1.0, + "content": "errs by using the function name instead of the prompt to generate the return statement (red).", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 10.5, + "bbox_fs": [ + 105, + 147, + 506, + 216 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 234, + 281, + 246 + ], + "lines": [ + { + "bbox": [ + 105, + 234, + 281, + 248 + ], + "spans": [ + { + "bbox": [ + 105, + 234, + 281, + 248 + ], + "score": 1.0, + "content": "3.3.3 Inspiration: Availability heuristic", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 14 + }, + { + "type": "text", + "bbox": [ + 107, + 253, + 505, + 297 + ], + "lines": [ + { + "bbox": [ + 105, + 253, + 506, + 266 + ], + "spans": [ + { + "bbox": [ + 105, + 253, + 506, + 266 + ], + "score": 1.0, + "content": "We next draw inspiration from the availability heuristic: the tendency of humans to evaluate how", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 264, + 506, + 276 + ], + "spans": [ + { + "bbox": [ + 105, + 264, + 506, + 276 + ], + "score": 1.0, + "content": "frequently an example occurs based on how easy it is to recall. For example, Tversky and Kahneman", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 107, + 275, + 505, + 288 + ], + "spans": [ + { + "bbox": [ + 107, + 275, + 135, + 287 + ], + "score": 0.56, + "content": "\\mathbb { \\underline { { \\lVert \\nabla ^ { 9 } 7 3 \\rVert } } }", + "type": "inline_equation" + }, + { + "bbox": [ + 135, + 275, + 505, + 288 + ], + "score": 1.0, + "content": "find that humans tend to incorrectly report that there are more first words that start with “r”", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 286, + 446, + 299 + ], + "spans": [ + { + "bbox": [ + 105, + 286, + 446, + 299 + ], + "score": 1.0, + "content": "and “k” than have third letter “r” and “k”, because the former quickly come to mind.", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 16.5, + "bbox_fs": [ + 105, + 253, + 506, + 299 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 302, + 505, + 390 + ], + "lines": [ + { + "bbox": [ + 105, + 302, + 505, + 316 + ], + "spans": [ + { + "bbox": [ + 105, + 302, + 505, + 316 + ], + "score": 1.0, + "content": "Using the availability heuristic as motivation, we hypothesize that code generation models may err by", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 313, + 505, + 326 + ], + "spans": [ + { + "bbox": [ + 105, + 313, + 505, + 326 + ], + "score": 1.0, + "content": "outputting solutions to related prompts that appear more frequently in the training set. To elicit this", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 324, + 505, + 338 + ], + "spans": [ + { + "bbox": [ + 105, + 324, + 505, + 338 + ], + "score": 1.0, + "content": "failure, we start with prompts that apply a unary operation before a binary operation (unary-first), then", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 335, + 505, + 349 + ], + "spans": [ + { + "bbox": [ + 105, + 335, + 505, + 349 + ], + "score": 1.0, + "content": "flip the order (binary-first). Programmers tend to apply unary operations first (e.g. when computing", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 346, + 505, + 359 + ], + "spans": [ + { + "bbox": [ + 105, + 346, + 505, + 359 + ], + "score": 1.0, + "content": "Euclidean distances or variances), so we conjecture that they appear more frequently on GitHub. We", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 357, + 505, + 370 + ], + "spans": [ + { + "bbox": [ + 105, + 357, + 505, + 370 + ], + "score": 1.0, + "content": "first check that flipping the order of operations decreases accuracy. Next, to test if code generation", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 369, + 505, + 381 + ], + "spans": [ + { + "bbox": [ + 105, + 369, + 505, + 381 + ], + "score": 1.0, + "content": "models instead outputs related prompts that occur more frequently in the training set, we measure", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 106, + 379, + 392, + 391 + ], + "spans": [ + { + "bbox": [ + 106, + 379, + 392, + 391 + ], + "score": 1.0, + "content": "whether code generation models instead output the unary-first solution.", + "type": "text" + } + ], + "index": 26 + } + ], + "index": 22.5, + "bbox_fs": [ + 105, + 302, + 505, + 391 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 395, + 505, + 473 + ], + "lines": [ + { + "bbox": [ + 105, + 394, + 505, + 408 + ], + "spans": [ + { + "bbox": [ + 105, + 394, + 505, + 408 + ], + "score": 1.0, + "content": "We consider all 12 combinations of the binary operations sum, difference, and product, with unary", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 405, + 505, + 420 + ], + "spans": [ + { + "bbox": [ + 105, + 405, + 505, + 420 + ], + "score": 1.0, + "content": "operations square, cube, quadruple, and square root. Focusing on Codex,5 we find that accuracy", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 417, + 505, + 429 + ], + "spans": [ + { + "bbox": [ + 106, + 417, + 151, + 429 + ], + "score": 1.0, + "content": "drops from", + "type": "text" + }, + { + "bbox": [ + 152, + 417, + 171, + 428 + ], + "score": 0.87, + "content": "50 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 171, + 417, + 182, + 429 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 182, + 417, + 201, + 428 + ], + "score": 0.87, + "content": "17 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 201, + 417, + 505, + 429 + ], + "score": 1.0, + "content": "when flipping the order from unary-first to binary-first. Among combinations", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 106, + 428, + 505, + 441 + ], + "spans": [ + { + "bbox": [ + 106, + 428, + 309, + 441 + ], + "score": 1.0, + "content": "where flipping the order leads to error, we find that", + "type": "text" + }, + { + "bbox": [ + 309, + 428, + 329, + 439 + ], + "score": 0.88, + "content": "7 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 329, + 428, + 505, + 441 + ], + "score": 1.0, + "content": "of the binary-first outputs are the unary-first", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 106, + 438, + 505, + 452 + ], + "spans": [ + { + "bbox": [ + 106, + 439, + 281, + 452 + ], + "score": 1.0, + "content": "solution. We exhibit one such error in Figure", + "type": "text" + }, + { + "bbox": [ + 282, + 438, + 292, + 451 + ], + "score": 0.46, + "content": "\\boxed { 5 }", + "type": "inline_equation" + }, + { + "bbox": [ + 292, + 439, + 505, + 452 + ], + "score": 1.0, + "content": "when prompted to square the sum of its inputs, Codex", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 450, + 505, + 462 + ], + "spans": [ + { + "bbox": [ + 105, + 450, + 505, + 462 + ], + "score": 1.0, + "content": "generates the correct function name (square_sum ), but reverses the order of operations. Our results", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 460, + 497, + 474 + ], + "spans": [ + { + "bbox": [ + 105, + 460, + 497, + 474 + ], + "score": 1.0, + "content": "suggest that Codex can err by outputting solutions to related, frequent prompts in the training set.", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 30, + "bbox_fs": [ + 105, + 394, + 505, + 474 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 477, + 505, + 522 + ], + "lines": [ + { + "bbox": [ + 106, + 477, + 506, + 489 + ], + "spans": [ + { + "bbox": [ + 106, + 477, + 506, + 489 + ], + "score": 1.0, + "content": "Control experiments. One worry is that the dip in performance is due the instructional nature of our", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 488, + 506, + 500 + ], + "spans": [ + { + "bbox": [ + 105, + 488, + 506, + 500 + ], + "score": 1.0, + "content": "prompts. We rule this out by evaluating Codex on prompts where the docstring appears beneath the", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 500, + 505, + 511 + ], + "spans": [ + { + "bbox": [ + 105, + 500, + 505, + 511 + ], + "score": 1.0, + "content": "function signature and is a definition rather than command, to more closely mimic some functions on", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 509, + 487, + 524 + ], + "spans": [ + { + "bbox": [ + 105, + 509, + 487, + 524 + ], + "score": 1.0, + "content": "GitHub. We obtain qualitatively similar results on these prompts, see Appendix A.4 for details.", + "type": "text" + } + ], + "index": 37 + } + ], + "index": 35.5, + "bbox_fs": [ + 105, + 477, + 506, + 524 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 533, + 284, + 544 + ], + "lines": [ + { + "bbox": [ + 106, + 533, + 285, + 546 + ], + "spans": [ + { + "bbox": [ + 106, + 533, + 285, + 546 + ], + "score": 1.0, + "content": "3.3.4 Inspiration: Attribute substitution", + "type": "text" + } + ], + "index": 38 + } + ], + "index": 38 + }, + { + "type": "text", + "bbox": [ + 107, + 551, + 505, + 595 + ], + "lines": [ + { + "bbox": [ + 105, + 550, + 506, + 564 + ], + "spans": [ + { + "bbox": [ + 105, + 550, + 506, + 564 + ], + "score": 1.0, + "content": "Finally, we draw inspiration from attribute substitution: the human tendency to respond to a com-", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 106, + 562, + 506, + 574 + ], + "spans": [ + { + "bbox": [ + 106, + 562, + 506, + 574 + ], + "score": 1.0, + "content": "plicated question using a simpler, related question [Kahneman and Frederick, 2002]. For example,", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 104, + 573, + 506, + 585 + ], + "spans": [ + { + "bbox": [ + 104, + 573, + 506, + 585 + ], + "score": 1.0, + "content": "a professor when asked how likely a candidate is to be tenured, may instead respond with how", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 585, + 253, + 595 + ], + "spans": [ + { + "bbox": [ + 105, + 585, + 253, + 595 + ], + "score": 1.0, + "content": "impressive they found their job talk.", + "type": "text" + } + ], + "index": 42 + } + ], + "index": 40.5, + "bbox_fs": [ + 104, + 550, + 506, + 595 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 600, + 505, + 666 + ], + "lines": [ + { + "bbox": [ + 105, + 600, + 505, + 613 + ], + "spans": [ + { + "bbox": [ + 105, + 600, + 505, + 613 + ], + "score": 1.0, + "content": "Using attribute substitution as inspiration, we hypothesize that Codex may use simple-but-incorrect", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 611, + 506, + 624 + ], + "spans": [ + { + "bbox": [ + 105, + 611, + 506, + 624 + ], + "score": 1.0, + "content": "heuristics to generate solutions. To elicit this failure, we add requests for conflicting function names", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 622, + 506, + 636 + ], + "spans": [ + { + "bbox": [ + 105, + 622, + 506, + 636 + ], + "score": 1.0, + "content": "to MathEquation prompts. For example, in Figure 5 we prompt Codex to write a program that sums", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 633, + 505, + 645 + ], + "spans": [ + { + "bbox": [ + 106, + 633, + 505, + 645 + ], + "score": 1.0, + "content": "its inputs called product_plus_2 . We first check that adding conflicting function names decreases", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 643, + 506, + 657 + ], + "spans": [ + { + "bbox": [ + 105, + 643, + 506, + 657 + ], + "score": 1.0, + "content": "Codex’s functional accuracy. Next, to test if Codex uses simple-but-incorrect heuristics to generate", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 106, + 655, + 419, + 667 + ], + "spans": [ + { + "bbox": [ + 106, + 655, + 419, + 667 + ], + "score": 1.0, + "content": "solutions, we check whether the generate solution matches the function name.", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 45.5, + "bbox_fs": [ + 105, + 600, + 506, + 667 + ] + }, + { + "type": "text", + "bbox": [ + 108, + 671, + 503, + 694 + ], + "lines": [ + { + "bbox": [ + 106, + 671, + 505, + 684 + ], + "spans": [ + { + "bbox": [ + 106, + 671, + 505, + 684 + ], + "score": 1.0, + "content": "We evaluate Codex using 90 MathEquation prompts where the desired solution and requested", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 106, + 682, + 505, + 695 + ], + "spans": [ + { + "bbox": [ + 106, + 682, + 505, + 695 + ], + "score": 1.0, + "content": "function name differ. To construct prompts, we begin with a prompt that Codex originally solves", + "type": "text" + } + ], + "index": 50 + } + ], + "index": 49.5, + "bbox_fs": [ + 106, + 671, + 505, + 695 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "table", + "bbox": [ + 167, + 69, + 445, + 130 + ], + "blocks": [ + { + "type": "table_body", + "bbox": [ + 167, + 69, + 445, + 130 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 167, + 69, + 445, + 130 + ], + "spans": [ + { + "bbox": [ + 167, + 69, + 445, + 130 + ], + "score": 0.971, + "html": "
Name locationCorrectMatches function nameOther error
No name100.0=0.0
Docstring4.480.015.6
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", + "type": "table", + "image_path": "81dcce63968dbcc3abba5ddacec3c1af131342a4d2b0f1dc64d31ff5f93f5c32.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 167, + 69, + 445, + 89.33333333333333 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 167, + 89.33333333333333, + 445, + 109.66666666666666 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 167, + 109.66666666666666, + 445, + 130.0 + ], + "spans": [], + "index": 2 + } + ] + } + ], + "index": 1 + }, + { + "type": "text", + "bbox": [ + 106, + 132, + 506, + 177 + ], + "lines": [ + { + "bbox": [ + 105, + 131, + 506, + 145 + ], + "spans": [ + { + "bbox": [ + 105, + 131, + 506, + 145 + ], + "score": 1.0, + "content": "Table 2: Results of the attribute substitution experiments. We report accuracy when we do not request", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 104, + 143, + 506, + 156 + ], + "spans": [ + { + "bbox": [ + 104, + 143, + 506, + 156 + ], + "score": 1.0, + "content": "a contradictory function name (no name), we request a function name in the docstring (docstring), in", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 154, + 507, + 167 + ], + "spans": [ + { + "bbox": [ + 105, + 154, + 507, + 167 + ], + "score": 1.0, + "content": "the function signature below the docstring (function signature), or above the docstring (name first).", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 164, + 456, + 178 + ], + "spans": [ + { + "bbox": [ + 105, + 164, + 456, + 178 + ], + "score": 1.0, + "content": "Overall, we find that Codex frequently generates solutions based on the function name.", + "type": "text" + } + ], + "index": 6 + } + ], + "index": 4.5 + }, + { + "type": "text", + "bbox": [ + 106, + 202, + 505, + 225 + ], + "lines": [ + { + "bbox": [ + 105, + 201, + 506, + 216 + ], + "spans": [ + { + "bbox": [ + 105, + 201, + 506, + 216 + ], + "score": 1.0, + "content": "(sum, difference, or product), then append a request for a specific, contradictory function name (see", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 212, + 295, + 226 + ], + "spans": [ + { + "bbox": [ + 105, + 213, + 146, + 226 + ], + "score": 1.0, + "content": "Appendix", + "type": "text" + }, + { + "bbox": [ + 146, + 212, + 166, + 226 + ], + "score": 0.29, + "content": "\\underline { { \\vert \\mathbf { A . } 4 \\vert } }", + "type": "inline_equation" + }, + { + "bbox": [ + 166, + 213, + 295, + 226 + ], + "score": 1.0, + "content": "for full implementation details).", + "type": "text" + } + ], + "index": 8 + } + ], + "index": 7.5 + }, + { + "type": "text", + "bbox": [ + 106, + 228, + 506, + 295 + ], + "lines": [ + { + "bbox": [ + 105, + 228, + 506, + 242 + ], + "spans": [ + { + "bbox": [ + 105, + 228, + 276, + 242 + ], + "score": 1.0, + "content": "We report our experimental results in Table", + "type": "text" + }, + { + "bbox": [ + 276, + 229, + 287, + 241 + ], + "score": 0.49, + "content": "\\triangledown", + "type": "inline_equation" + }, + { + "bbox": [ + 287, + 228, + 506, + 242 + ], + "score": 1.0, + "content": "When we request a conflicting function name, Codex’s", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 240, + 505, + 253 + ], + "spans": [ + { + "bbox": [ + 106, + 240, + 191, + 253 + ], + "score": 1.0, + "content": "accuracy drops from", + "type": "text" + }, + { + "bbox": [ + 192, + 241, + 216, + 251 + ], + "score": 0.87, + "content": "100 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 216, + 240, + 247, + 253 + ], + "score": 1.0, + "content": "to only", + "type": "text" + }, + { + "bbox": [ + 248, + 241, + 294, + 251 + ], + "score": 0.83, + "content": "4 . 4 \\% - 4 . 6 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 294, + 240, + 505, + 253 + ], + "score": 1.0, + "content": ". This finding holds whether we request the function", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 252, + 505, + 263 + ], + "spans": [ + { + "bbox": [ + 105, + 252, + 505, + 263 + ], + "score": 1.0, + "content": "name in the docstring, write it in the function signature below the docstring, or write the function", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 104, + 261, + 507, + 276 + ], + "spans": [ + { + "bbox": [ + 104, + 261, + 397, + 276 + ], + "score": 1.0, + "content": "name over a simple description on the function. Moreover, for between", + "type": "text" + }, + { + "bbox": [ + 397, + 262, + 417, + 273 + ], + "score": 0.87, + "content": "52 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 418, + 261, + 435, + 276 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 436, + 262, + 456, + 273 + ], + "score": 0.87, + "content": "80 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 456, + 261, + 507, + 276 + ], + "score": 1.0, + "content": "of prompts,", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 274, + 505, + 285 + ], + "spans": [ + { + "bbox": [ + 106, + 274, + 505, + 285 + ], + "score": 1.0, + "content": "Codex responds with the function specified in the function name. Our results indicate that Codex can", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 285, + 368, + 297 + ], + "spans": [ + { + "bbox": [ + 106, + 285, + 368, + 297 + ], + "score": 1.0, + "content": "err by using simple-but-incorrect heuristics to generate solutions.", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 11.5 + }, + { + "type": "title", + "bbox": [ + 107, + 310, + 200, + 323 + ], + "lines": [ + { + "bbox": [ + 104, + 308, + 201, + 326 + ], + "spans": [ + { + "bbox": [ + 104, + 308, + 201, + 326 + ], + "score": 1.0, + "content": "4 GPT-3 Results", + "type": "text" + } + ], + "index": 15 + } + ], + "index": 15 + }, + { + "type": "text", + "bbox": [ + 107, + 334, + 505, + 368 + ], + "lines": [ + { + "bbox": [ + 104, + 332, + 506, + 348 + ], + "spans": [ + { + "bbox": [ + 104, + 332, + 506, + 348 + ], + "score": 1.0, + "content": "In this section, we extend our study from Codex to GPT-3. To test GPT-3 for failure modes, we try", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 344, + 506, + 358 + ], + "spans": [ + { + "bbox": [ + 105, + 344, + 506, + 358 + ], + "score": 1.0, + "content": "to faithfully reproduce and extend the anchoring experiment of Jacowitz and Kahneman [1995] and", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 356, + 349, + 369 + ], + "spans": [ + { + "bbox": [ + 105, + 356, + 349, + 369 + ], + "score": 1.0, + "content": "framing effect experiment of Tversky and Kahneman [1981].", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 17 + }, + { + "type": "text", + "bbox": [ + 106, + 372, + 505, + 416 + ], + "lines": [ + { + "bbox": [ + 105, + 371, + 505, + 386 + ], + "spans": [ + { + "bbox": [ + 105, + 371, + 505, + 386 + ], + "score": 1.0, + "content": "Anchoring. As in Section 3.3.2 we study Section 3.3.2, we study anchoring: humans’ tendency", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 383, + 504, + 397 + ], + "spans": [ + { + "bbox": [ + 105, + 383, + 504, + 397 + ], + "score": 1.0, + "content": "to insufficiently adjust their estimates away from an initial value [Tversky and Kahneman, 1974].", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 104, + 393, + 505, + 407 + ], + "spans": [ + { + "bbox": [ + 104, + 393, + 505, + 407 + ], + "score": 1.0, + "content": "We largely replicate the anchoring study presented in Jacowitz and Kahneman [1995], but test the", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 104, + 404, + 355, + 418 + ], + "spans": [ + { + "bbox": [ + 104, + 404, + 355, + 418 + ], + "score": 1.0, + "content": "“davinci-001” version of OpenAI’s GPT-3 instead of humans.", + "type": "text" + } + ], + "index": 22 + } + ], + "index": 20.5 + }, + { + "type": "text", + "bbox": [ + 107, + 421, + 505, + 477 + ], + "lines": [ + { + "bbox": [ + 106, + 422, + 505, + 434 + ], + "spans": [ + { + "bbox": [ + 106, + 422, + 505, + 434 + ], + "score": 1.0, + "content": "In their original experiment, Jacowitz and Kahneman asked students to estimate quantities such as the", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 432, + 506, + 446 + ], + "spans": [ + { + "bbox": [ + 106, + 432, + 506, + 446 + ], + "score": 1.0, + "content": "length of the Mississippi river in miles. They then asked new students to estimate the same quantities,", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 442, + 506, + 457 + ], + "spans": [ + { + "bbox": [ + 105, + 442, + 506, + 457 + ], + "score": 1.0, + "content": "but first gave them a upper or lower bound on the true answer (e.g. the Mississippi river is longer than", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 453, + 505, + 468 + ], + "spans": [ + { + "bbox": [ + 105, + 453, + 505, + 468 + ], + "score": 1.0, + "content": "700 miles), which they call anchors. They find that students tend to underestimate the true quantity", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 465, + 497, + 479 + ], + "spans": [ + { + "bbox": [ + 105, + 465, + 497, + 479 + ], + "score": 1.0, + "content": "when prompted with the lower anchor, and overestimate it when prompted with the upper anchor.", + "type": "text" + } + ], + "index": 27 + } + ], + "index": 25 + }, + { + "type": "text", + "bbox": [ + 107, + 481, + 505, + 537 + ], + "lines": [ + { + "bbox": [ + 106, + 482, + 505, + 494 + ], + "spans": [ + { + "bbox": [ + 106, + 482, + 505, + 494 + ], + "score": 1.0, + "content": "We adapt the anchoring study from Jacowitz and Kahneman [1995] by finding the true answer for 14", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 490, + 506, + 506 + ], + "spans": [ + { + "bbox": [ + 105, + 490, + 506, + 506 + ], + "score": 1.0, + "content": "of their 15 original questions6, then computing upper and lower anchors by increasing and decreasing", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 503, + 507, + 516 + ], + "spans": [ + { + "bbox": [ + 105, + 503, + 253, + 516 + ], + "score": 1.0, + "content": "the true answer by a fixed percentage", + "type": "text" + }, + { + "bbox": [ + 253, + 506, + 259, + 515 + ], + "score": 0.68, + "content": "p", + "type": "inline_equation" + }, + { + "bbox": [ + 259, + 503, + 319, + 516 + ], + "score": 1.0, + "content": ". See Appendix", + "type": "text" + }, + { + "bbox": [ + 320, + 503, + 337, + 514 + ], + "score": 0.82, + "content": "\\mathbf { B . l }", + "type": "inline_equation" + }, + { + "bbox": [ + 338, + 503, + 507, + 516 + ], + "score": 1.0, + "content": "for a full list of questions and true answers.", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 106, + 515, + 506, + 527 + ], + "spans": [ + { + "bbox": [ + 106, + 515, + 303, + 527 + ], + "score": 1.0, + "content": "As an example, if the actual answer is 2000 and", + "type": "text" + }, + { + "bbox": [ + 303, + 516, + 310, + 526 + ], + "score": 0.81, + "content": "p", + "type": "inline_equation" + }, + { + "bbox": [ + 310, + 515, + 320, + 527 + ], + "score": 1.0, + "content": "is", + "type": "text" + }, + { + "bbox": [ + 320, + 515, + 340, + 525 + ], + "score": 0.87, + "content": "5 0 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 340, + 515, + 506, + 527 + ], + "score": 1.0, + "content": ", the upper anchor is 3000 and the lower", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 524, + 438, + 538 + ], + "spans": [ + { + "bbox": [ + 105, + 524, + 438, + 538 + ], + "score": 1.0, + "content": "anchor is 1000. We use this bound as an anchor, so that a typical prompt might be:", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 30 + }, + { + "type": "text", + "bbox": [ + 173, + 571, + 427, + 583 + ], + "lines": [ + { + "bbox": [ + 172, + 570, + 428, + 584 + ], + "spans": [ + { + "bbox": [ + 172, + 570, + 428, + 584 + ], + "score": 1.0, + "content": "What is the length of the Mississippi River (in miles)? Answer:", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 33 + }, + { + "type": "text", + "bbox": [ + 106, + 589, + 505, + 667 + ], + "lines": [ + { + "bbox": [ + 105, + 588, + 506, + 602 + ], + "spans": [ + { + "bbox": [ + 105, + 588, + 506, + 602 + ], + "score": 1.0, + "content": "To study anchoring in GPT-3, we measure how prepending the anchor changes GPT-3’s estimate.", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 106, + 600, + 505, + 613 + ], + "spans": [ + { + "bbox": [ + 106, + 600, + 505, + 613 + ], + "score": 1.0, + "content": "We categorize four potential changes: the estimate does not change, the estimate shifts towards the", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 611, + 505, + 624 + ], + "spans": [ + { + "bbox": [ + 106, + 611, + 505, + 624 + ], + "score": 1.0, + "content": "anchor, the estimate shifts away from the anchor, and the estimate is gibberish. We report the results", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 621, + 505, + 635 + ], + "spans": [ + { + "bbox": [ + 105, + 622, + 140, + 635 + ], + "score": 1.0, + "content": "in Table", + "type": "text" + }, + { + "bbox": [ + 140, + 621, + 150, + 635 + ], + "score": 0.7, + "content": "^ 3 .", + "type": "inline_equation" + }, + { + "bbox": [ + 150, + 622, + 163, + 635 + ], + "score": 1.0, + "content": "for", + "type": "text" + }, + { + "bbox": [ + 163, + 622, + 233, + 634 + ], + "score": 0.85, + "content": "p \\in \\{ 2 0 \\% , 5 0 \\% \\}", + "type": "inline_equation" + }, + { + "bbox": [ + 233, + 622, + 505, + 635 + ], + "score": 1.0, + "content": ". We find that GPT-3 routinely updates its estimate when an anchor", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 104, + 632, + 506, + 645 + ], + "spans": [ + { + "bbox": [ + 104, + 632, + 506, + 645 + ], + "score": 1.0, + "content": "is prepended, and tends to shift the estimate towards the anchor. We also find that while GPT-3’s", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 644, + 506, + 656 + ], + "spans": [ + { + "bbox": [ + 105, + 644, + 329, + 656 + ], + "score": 1.0, + "content": "updated estimate sometimes matches the anchor exactly", + "type": "text" + }, + { + "bbox": [ + 329, + 644, + 348, + 655 + ], + "score": 0.86, + "content": "67 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 349, + 644, + 506, + 656 + ], + "score": 1.0, + "content": "of the time), it also often lands between", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 106, + 655, + 401, + 667 + ], + "spans": [ + { + "bbox": [ + 106, + 655, + 401, + 667 + ], + "score": 1.0, + "content": "the anchor and the original prediction, mirroring the behavior of humans.", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 37 + }, + { + "type": "text", + "bbox": [ + 108, + 671, + 505, + 705 + ], + "lines": [ + { + "bbox": [ + 106, + 670, + 506, + 684 + ], + "spans": [ + { + "bbox": [ + 106, + 670, + 506, + 684 + ], + "score": 1.0, + "content": "Our replication has a few limitations. Like the original study our sample size is small, we construct", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 106, + 682, + 507, + 696 + ], + "spans": [ + { + "bbox": [ + 106, + 682, + 361, + 696 + ], + "score": 1.0, + "content": "prompts with templates, and many of the outputs—on average", + "type": "text" + }, + { + "bbox": [ + 362, + 682, + 381, + 693 + ], + "score": 0.86, + "content": "41 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 382, + 682, + 507, + 696 + ], + "score": 1.0, + "content": "—are gibberish. Nevertheless,", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 106, + 694, + 400, + 705 + ], + "spans": [ + { + "bbox": [ + 106, + 694, + 400, + 705 + ], + "score": 1.0, + "content": "our results suggest that GPT-3 incorporates the anchor during estimation.", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 42 + } + ], + "page_idx": 7, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 302, + 742, + 308, + 750 + ], + "lines": [ + { + "bbox": [ + 301, + 740, + 310, + 752 + ], + "spans": [ + { + "bbox": [ + 301, + 740, + 310, + 752 + ], + "score": 1.0, + "content": "", + "type": "text", + "height": 12, + "width": 9 + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 115, + 711, + 475, + 722 + ], + "lines": [ + { + "bbox": [ + 118, + 709, + 477, + 724 + ], + "spans": [ + { + "bbox": [ + 118, + 709, + 477, + 724 + ], + "score": 1.0, + "content": "6 We omit a question asking for the number of bars in Berkeley, CA, since the answer is ambiguous", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "table", + "bbox": [ + 167, + 69, + 445, + 130 + ], + "blocks": [ + { + "type": "table_body", + "bbox": [ + 167, + 69, + 445, + 130 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 167, + 69, + 445, + 130 + ], + "spans": [ + { + "bbox": [ + 167, + 69, + 445, + 130 + ], + "score": 0.971, + "html": "
Name locationCorrectMatches function nameOther error
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", + "type": "table", + "image_path": "81dcce63968dbcc3abba5ddacec3c1af131342a4d2b0f1dc64d31ff5f93f5c32.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 167, + 69, + 445, + 89.33333333333333 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 167, + 89.33333333333333, + 445, + 109.66666666666666 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 167, + 109.66666666666666, + 445, + 130.0 + ], + "spans": [], + "index": 2 + } + ] + } + ], + "index": 1 + }, + { + "type": "text", + "bbox": [ + 106, + 132, + 506, + 177 + ], + "lines": [ + { + "bbox": [ + 105, + 131, + 506, + 145 + ], + "spans": [ + { + "bbox": [ + 105, + 131, + 506, + 145 + ], + "score": 1.0, + "content": "Table 2: Results of the attribute substitution experiments. We report accuracy when we do not request", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 104, + 143, + 506, + 156 + ], + "spans": [ + { + "bbox": [ + 104, + 143, + 506, + 156 + ], + "score": 1.0, + "content": "a contradictory function name (no name), we request a function name in the docstring (docstring), in", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 154, + 507, + 167 + ], + "spans": [ + { + "bbox": [ + 105, + 154, + 507, + 167 + ], + "score": 1.0, + "content": "the function signature below the docstring (function signature), or above the docstring (name first).", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 164, + 456, + 178 + ], + "spans": [ + { + "bbox": [ + 105, + 164, + 456, + 178 + ], + "score": 1.0, + "content": "Overall, we find that Codex frequently generates solutions based on the function name.", + "type": "text" + } + ], + "index": 6 + } + ], + "index": 4.5, + "bbox_fs": [ + 104, + 131, + 507, + 178 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 202, + 505, + 225 + ], + "lines": [ + { + "bbox": [ + 105, + 201, + 506, + 216 + ], + "spans": [ + { + "bbox": [ + 105, + 201, + 506, + 216 + ], + "score": 1.0, + "content": "(sum, difference, or product), then append a request for a specific, contradictory function name (see", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 212, + 295, + 226 + ], + "spans": [ + { + "bbox": [ + 105, + 213, + 146, + 226 + ], + "score": 1.0, + "content": "Appendix", + "type": "text" + }, + { + "bbox": [ + 146, + 212, + 166, + 226 + ], + "score": 0.29, + "content": "\\underline { { \\vert \\mathbf { A . } 4 \\vert } }", + "type": "inline_equation" + }, + { + "bbox": [ + 166, + 213, + 295, + 226 + ], + "score": 1.0, + "content": "for full implementation details).", + "type": "text" + } + ], + "index": 8 + } + ], + "index": 7.5, + "bbox_fs": [ + 105, + 201, + 506, + 226 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 228, + 506, + 295 + ], + "lines": [ + { + "bbox": [ + 105, + 228, + 506, + 242 + ], + "spans": [ + { + "bbox": [ + 105, + 228, + 276, + 242 + ], + "score": 1.0, + "content": "We report our experimental results in Table", + "type": "text" + }, + { + "bbox": [ + 276, + 229, + 287, + 241 + ], + "score": 0.49, + "content": "\\triangledown", + "type": "inline_equation" + }, + { + "bbox": [ + 287, + 228, + 506, + 242 + ], + "score": 1.0, + "content": "When we request a conflicting function name, Codex’s", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 240, + 505, + 253 + ], + "spans": [ + { + "bbox": [ + 106, + 240, + 191, + 253 + ], + "score": 1.0, + "content": "accuracy drops from", + "type": "text" + }, + { + "bbox": [ + 192, + 241, + 216, + 251 + ], + "score": 0.87, + "content": "100 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 216, + 240, + 247, + 253 + ], + "score": 1.0, + "content": "to only", + "type": "text" + }, + { + "bbox": [ + 248, + 241, + 294, + 251 + ], + "score": 0.83, + "content": "4 . 4 \\% - 4 . 6 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 294, + 240, + 505, + 253 + ], + "score": 1.0, + "content": ". This finding holds whether we request the function", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 252, + 505, + 263 + ], + "spans": [ + { + "bbox": [ + 105, + 252, + 505, + 263 + ], + "score": 1.0, + "content": "name in the docstring, write it in the function signature below the docstring, or write the function", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 104, + 261, + 507, + 276 + ], + "spans": [ + { + "bbox": [ + 104, + 261, + 397, + 276 + ], + "score": 1.0, + "content": "name over a simple description on the function. Moreover, for between", + "type": "text" + }, + { + "bbox": [ + 397, + 262, + 417, + 273 + ], + "score": 0.87, + "content": "52 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 418, + 261, + 435, + 276 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 436, + 262, + 456, + 273 + ], + "score": 0.87, + "content": "80 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 456, + 261, + 507, + 276 + ], + "score": 1.0, + "content": "of prompts,", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 274, + 505, + 285 + ], + "spans": [ + { + "bbox": [ + 106, + 274, + 505, + 285 + ], + "score": 1.0, + "content": "Codex responds with the function specified in the function name. Our results indicate that Codex can", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 285, + 368, + 297 + ], + "spans": [ + { + "bbox": [ + 106, + 285, + 368, + 297 + ], + "score": 1.0, + "content": "err by using simple-but-incorrect heuristics to generate solutions.", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 11.5, + "bbox_fs": [ + 104, + 228, + 507, + 297 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 310, + 200, + 323 + ], + "lines": [ + { + "bbox": [ + 104, + 308, + 201, + 326 + ], + "spans": [ + { + "bbox": [ + 104, + 308, + 201, + 326 + ], + "score": 1.0, + "content": "4 GPT-3 Results", + "type": "text" + } + ], + "index": 15 + } + ], + "index": 15 + }, + { + "type": "text", + "bbox": [ + 107, + 334, + 505, + 368 + ], + "lines": [ + { + "bbox": [ + 104, + 332, + 506, + 348 + ], + "spans": [ + { + "bbox": [ + 104, + 332, + 506, + 348 + ], + "score": 1.0, + "content": "In this section, we extend our study from Codex to GPT-3. To test GPT-3 for failure modes, we try", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 344, + 506, + 358 + ], + "spans": [ + { + "bbox": [ + 105, + 344, + 506, + 358 + ], + "score": 1.0, + "content": "to faithfully reproduce and extend the anchoring experiment of Jacowitz and Kahneman [1995] and", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 356, + 349, + 369 + ], + "spans": [ + { + "bbox": [ + 105, + 356, + 349, + 369 + ], + "score": 1.0, + "content": "framing effect experiment of Tversky and Kahneman [1981].", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 17, + "bbox_fs": [ + 104, + 332, + 506, + 369 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 372, + 505, + 416 + ], + "lines": [ + { + "bbox": [ + 105, + 371, + 505, + 386 + ], + "spans": [ + { + "bbox": [ + 105, + 371, + 505, + 386 + ], + "score": 1.0, + "content": "Anchoring. As in Section 3.3.2 we study Section 3.3.2, we study anchoring: humans’ tendency", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 383, + 504, + 397 + ], + "spans": [ + { + "bbox": [ + 105, + 383, + 504, + 397 + ], + "score": 1.0, + "content": "to insufficiently adjust their estimates away from an initial value [Tversky and Kahneman, 1974].", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 104, + 393, + 505, + 407 + ], + "spans": [ + { + "bbox": [ + 104, + 393, + 505, + 407 + ], + "score": 1.0, + "content": "We largely replicate the anchoring study presented in Jacowitz and Kahneman [1995], but test the", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 104, + 404, + 355, + 418 + ], + "spans": [ + { + "bbox": [ + 104, + 404, + 355, + 418 + ], + "score": 1.0, + "content": "“davinci-001” version of OpenAI’s GPT-3 instead of humans.", + "type": "text" + } + ], + "index": 22 + } + ], + "index": 20.5, + "bbox_fs": [ + 104, + 371, + 505, + 418 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 421, + 505, + 477 + ], + "lines": [ + { + "bbox": [ + 106, + 422, + 505, + 434 + ], + "spans": [ + { + "bbox": [ + 106, + 422, + 505, + 434 + ], + "score": 1.0, + "content": "In their original experiment, Jacowitz and Kahneman asked students to estimate quantities such as the", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 432, + 506, + 446 + ], + "spans": [ + { + "bbox": [ + 106, + 432, + 506, + 446 + ], + "score": 1.0, + "content": "length of the Mississippi river in miles. They then asked new students to estimate the same quantities,", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 442, + 506, + 457 + ], + "spans": [ + { + "bbox": [ + 105, + 442, + 506, + 457 + ], + "score": 1.0, + "content": "but first gave them a upper or lower bound on the true answer (e.g. the Mississippi river is longer than", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 453, + 505, + 468 + ], + "spans": [ + { + "bbox": [ + 105, + 453, + 505, + 468 + ], + "score": 1.0, + "content": "700 miles), which they call anchors. They find that students tend to underestimate the true quantity", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 465, + 497, + 479 + ], + "spans": [ + { + "bbox": [ + 105, + 465, + 497, + 479 + ], + "score": 1.0, + "content": "when prompted with the lower anchor, and overestimate it when prompted with the upper anchor.", + "type": "text" + } + ], + "index": 27 + } + ], + "index": 25, + "bbox_fs": [ + 105, + 422, + 506, + 479 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 481, + 505, + 537 + ], + "lines": [ + { + "bbox": [ + 106, + 482, + 505, + 494 + ], + "spans": [ + { + "bbox": [ + 106, + 482, + 505, + 494 + ], + "score": 1.0, + "content": "We adapt the anchoring study from Jacowitz and Kahneman [1995] by finding the true answer for 14", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 490, + 506, + 506 + ], + "spans": [ + { + "bbox": [ + 105, + 490, + 506, + 506 + ], + "score": 1.0, + "content": "of their 15 original questions6, then computing upper and lower anchors by increasing and decreasing", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 503, + 507, + 516 + ], + "spans": [ + { + "bbox": [ + 105, + 503, + 253, + 516 + ], + "score": 1.0, + "content": "the true answer by a fixed percentage", + "type": "text" + }, + { + "bbox": [ + 253, + 506, + 259, + 515 + ], + "score": 0.68, + "content": "p", + "type": "inline_equation" + }, + { + "bbox": [ + 259, + 503, + 319, + 516 + ], + "score": 1.0, + "content": ". See Appendix", + "type": "text" + }, + { + "bbox": [ + 320, + 503, + 337, + 514 + ], + "score": 0.82, + "content": "\\mathbf { B . l }", + "type": "inline_equation" + }, + { + "bbox": [ + 338, + 503, + 507, + 516 + ], + "score": 1.0, + "content": "for a full list of questions and true answers.", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 106, + 515, + 506, + 527 + ], + "spans": [ + { + "bbox": [ + 106, + 515, + 303, + 527 + ], + "score": 1.0, + "content": "As an example, if the actual answer is 2000 and", + "type": "text" + }, + { + "bbox": [ + 303, + 516, + 310, + 526 + ], + "score": 0.81, + "content": "p", + "type": "inline_equation" + }, + { + "bbox": [ + 310, + 515, + 320, + 527 + ], + "score": 1.0, + "content": "is", + "type": "text" + }, + { + "bbox": [ + 320, + 515, + 340, + 525 + ], + "score": 0.87, + "content": "5 0 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 340, + 515, + 506, + 527 + ], + "score": 1.0, + "content": ", the upper anchor is 3000 and the lower", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 524, + 438, + 538 + ], + "spans": [ + { + "bbox": [ + 105, + 524, + 438, + 538 + ], + "score": 1.0, + "content": "anchor is 1000. We use this bound as an anchor, so that a typical prompt might be:", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 30, + "bbox_fs": [ + 105, + 482, + 507, + 538 + ] + }, + { + "type": "text", + "bbox": [ + 173, + 571, + 427, + 583 + ], + "lines": [ + { + "bbox": [ + 172, + 570, + 428, + 584 + ], + "spans": [ + { + "bbox": [ + 172, + 570, + 428, + 584 + ], + "score": 1.0, + "content": "What is the length of the Mississippi River (in miles)? Answer:", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 33, + "bbox_fs": [ + 172, + 570, + 428, + 584 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 589, + 505, + 667 + ], + "lines": [ + { + "bbox": [ + 105, + 588, + 506, + 602 + ], + "spans": [ + { + "bbox": [ + 105, + 588, + 506, + 602 + ], + "score": 1.0, + "content": "To study anchoring in GPT-3, we measure how prepending the anchor changes GPT-3’s estimate.", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 106, + 600, + 505, + 613 + ], + "spans": [ + { + "bbox": [ + 106, + 600, + 505, + 613 + ], + "score": 1.0, + "content": "We categorize four potential changes: the estimate does not change, the estimate shifts towards the", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 611, + 505, + 624 + ], + "spans": [ + { + "bbox": [ + 106, + 611, + 505, + 624 + ], + "score": 1.0, + "content": "anchor, the estimate shifts away from the anchor, and the estimate is gibberish. We report the results", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 621, + 505, + 635 + ], + "spans": [ + { + "bbox": [ + 105, + 622, + 140, + 635 + ], + "score": 1.0, + "content": "in Table", + "type": "text" + }, + { + "bbox": [ + 140, + 621, + 150, + 635 + ], + "score": 0.7, + "content": "^ 3 .", + "type": "inline_equation" + }, + { + "bbox": [ + 150, + 622, + 163, + 635 + ], + "score": 1.0, + "content": "for", + "type": "text" + }, + { + "bbox": [ + 163, + 622, + 233, + 634 + ], + "score": 0.85, + "content": "p \\in \\{ 2 0 \\% , 5 0 \\% \\}", + "type": "inline_equation" + }, + { + "bbox": [ + 233, + 622, + 505, + 635 + ], + "score": 1.0, + "content": ". We find that GPT-3 routinely updates its estimate when an anchor", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 104, + 632, + 506, + 645 + ], + "spans": [ + { + "bbox": [ + 104, + 632, + 506, + 645 + ], + "score": 1.0, + "content": "is prepended, and tends to shift the estimate towards the anchor. We also find that while GPT-3’s", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 644, + 506, + 656 + ], + "spans": [ + { + "bbox": [ + 105, + 644, + 329, + 656 + ], + "score": 1.0, + "content": "updated estimate sometimes matches the anchor exactly", + "type": "text" + }, + { + "bbox": [ + 329, + 644, + 348, + 655 + ], + "score": 0.86, + "content": "67 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 349, + 644, + 506, + 656 + ], + "score": 1.0, + "content": "of the time), it also often lands between", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 106, + 655, + 401, + 667 + ], + "spans": [ + { + "bbox": [ + 106, + 655, + 401, + 667 + ], + "score": 1.0, + "content": "the anchor and the original prediction, mirroring the behavior of humans.", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 37, + "bbox_fs": [ + 104, + 588, + 506, + 667 + ] + }, + { + "type": "text", + "bbox": [ + 108, + 671, + 505, + 705 + ], + "lines": [ + { + "bbox": [ + 106, + 670, + 506, + 684 + ], + "spans": [ + { + "bbox": [ + 106, + 670, + 506, + 684 + ], + "score": 1.0, + "content": "Our replication has a few limitations. Like the original study our sample size is small, we construct", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 106, + 682, + 507, + 696 + ], + "spans": [ + { + "bbox": [ + 106, + 682, + 361, + 696 + ], + "score": 1.0, + "content": "prompts with templates, and many of the outputs—on average", + "type": "text" + }, + { + "bbox": [ + 362, + 682, + 381, + 693 + ], + "score": 0.86, + "content": "41 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 382, + 682, + 507, + 696 + ], + "score": 1.0, + "content": "—are gibberish. Nevertheless,", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 106, + 694, + 400, + 705 + ], + "spans": [ + { + "bbox": [ + 106, + 694, + 400, + 705 + ], + "score": 1.0, + "content": "our results suggest that GPT-3 incorporates the anchor during estimation.", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 42, + "bbox_fs": [ + 106, + 670, + 507, + 705 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "table", + "bbox": [ + 169, + 80, + 441, + 119 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 258, + 71, + 381, + 80 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 258, + 70, + 381, + 82 + ], + "spans": [ + { + "bbox": [ + 258, + 70, + 381, + 82 + ], + "score": 1.0, + "content": "Anchor change to model output", + "type": "text" + } + ], + "index": 0 + } + ], + "index": 0 + }, + { + "type": "table_body", + "bbox": [ + 169, + 80, + 441, + 119 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 169, + 80, + 441, + 119 + ], + "spans": [ + { + "bbox": [ + 169, + 80, + 441, + 119 + ], + "score": 0.94, + "html": "
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Example where Codex incorrectly deletes files. We prompt Codex to delete files", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 300, + 505, + 312 + ], + "spans": [ + { + "bbox": [ + 105, + 300, + 505, + 312 + ], + "score": 1.0, + "content": "containing all of statsmodels, plotly, seaborn, and scipy. Codex correctly iterates through all files", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 311, + 505, + 324 + ], + "spans": [ + { + "bbox": [ + 105, + 311, + 505, + 324 + ], + "score": 1.0, + "content": "in the inputted directory (blue), but then incorrectly deletes all files containing statsmodels (red), as", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 322, + 505, + 334 + ], + "spans": [ + { + "bbox": [ + 105, + 322, + 505, + 334 + ], + "score": 1.0, + "content": "attribute substitution suggests. Right. Plot describing the errors Codex makes as a function of the", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 106, + 333, + 505, + 344 + ], + "spans": [ + { + "bbox": [ + 106, + 333, + 505, + 344 + ], + "score": 1.0, + "content": "number of packages. We find that Codex often incorrectly deletes files if they contain any of the", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 104, + 342, + 483, + 357 + ], + "spans": [ + { + "bbox": [ + 104, + 342, + 483, + 357 + ], + "score": 1.0, + "content": "listed packages, and relies more on just the first package as the number of packages increases.", + "type": "text" + } + ], + "index": 27 + } + ], + "index": 24.5 + } + ], + "index": 20.5 + }, + { + "type": "text", + "bbox": [ + 106, + 377, + 505, + 433 + ], + "lines": [ + { + "bbox": [ + 106, + 378, + 505, + 391 + ], + "spans": [ + { + "bbox": [ + 106, + 378, + 238, + 391 + ], + "score": 1.0, + "content": "Framing effect. As in Section", + "type": "text" + }, + { + "bbox": [ + 239, + 378, + 264, + 391 + ], + "score": 0.82, + "content": "3 . 3 . 1 ,", + "type": "inline_equation" + }, + { + "bbox": [ + 264, + 378, + 505, + 391 + ], + "score": 1.0, + "content": "we study the framing effect: predictable shifts in human", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 389, + 506, + 401 + ], + "spans": [ + { + "bbox": [ + 106, + 389, + 506, + 401 + ], + "score": 1.0, + "content": "responses when the same problem is framed in different ways. We largely replicate the framing exper-", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 106, + 399, + 506, + 413 + ], + "spans": [ + { + "bbox": [ + 106, + 399, + 506, + 413 + ], + "score": 1.0, + "content": "iment presented in Tversky and Kahneman [1981]: we compare GPT-3’s responses to two equivalent", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 410, + 506, + 424 + ], + "spans": [ + { + "bbox": [ + 105, + 410, + 506, + 424 + ], + "score": 1.0, + "content": "decisions: choosing to either deterministically save (or let die) some fraction of a population, or to", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 422, + 315, + 434 + ], + "spans": [ + { + "bbox": [ + 105, + 422, + 315, + 434 + ], + "score": 1.0, + "content": "probabilistically save (let die) the whole population.", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 30 + }, + { + "type": "text", + "bbox": [ + 106, + 438, + 505, + 526 + ], + "lines": [ + { + "bbox": [ + 106, + 438, + 505, + 450 + ], + "spans": [ + { + "bbox": [ + 106, + 438, + 505, + 450 + ], + "score": 1.0, + "content": "We measure the rate at which GPT-3 chooses the probabilistic option across different population sizes", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 106, + 449, + 505, + 462 + ], + "spans": [ + { + "bbox": [ + 106, + 450, + 310, + 462 + ], + "score": 1.0, + "content": "and different fractions / probabilities. See Section", + "type": "text" + }, + { + "bbox": [ + 310, + 449, + 329, + 461 + ], + "score": 0.5, + "content": "\\boxed { \\mathbf { B } . 2 }", + "type": "inline_equation" + }, + { + "bbox": [ + 329, + 450, + 505, + 462 + ], + "score": 1.0, + "content": "for full results. When using the probability", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 106, + 460, + 505, + 473 + ], + "spans": [ + { + "bbox": [ + 106, + 460, + 505, + 473 + ], + "score": 1.0, + "content": "in the original study, GPT-3 qualitatively mirrors humans: it chooses the probabilistic option far", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 471, + 505, + 483 + ], + "spans": [ + { + "bbox": [ + 105, + 471, + 505, + 483 + ], + "score": 1.0, + "content": "more frequently under the “not save” framing than under the “save framing”. However, for higher", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 482, + 505, + 495 + ], + "spans": [ + { + "bbox": [ + 105, + 482, + 505, + 495 + ], + "score": 1.0, + "content": "probabilities, GPT-3 consistently chooses the probabilistic option for both framings; we conjecture", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 492, + 505, + 506 + ], + "spans": [ + { + "bbox": [ + 105, + 492, + 505, + 506 + ], + "score": 1.0, + "content": "that humans could exhibit similar behavior in this regime, since the probabilistic option is more", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 106, + 504, + 505, + 516 + ], + "spans": [ + { + "bbox": [ + 106, + 504, + 505, + 516 + ], + "score": 1.0, + "content": "certain. Overall, our results suggest that GPT-3 selects different options based on the framing, and", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 106, + 515, + 488, + 527 + ], + "spans": [ + { + "bbox": [ + 106, + 515, + 488, + 527 + ], + "score": 1.0, + "content": "could be a test-bed to identify qualitative human behaviors without running full human studies.", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 36.5 + }, + { + "type": "title", + "bbox": [ + 107, + 543, + 229, + 557 + ], + "lines": [ + { + "bbox": [ + 104, + 542, + 231, + 560 + ], + "spans": [ + { + "bbox": [ + 104, + 542, + 231, + 560 + ], + "score": 1.0, + "content": "5 High-Impact Errors", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 41 + }, + { + "type": "text", + "bbox": [ + 108, + 568, + 505, + 602 + ], + "lines": [ + { + "bbox": [ + 105, + 568, + 505, + 582 + ], + "spans": [ + { + "bbox": [ + 105, + 568, + 505, + 582 + ], + "score": 1.0, + "content": "We have shown how our framework helps us elicit failures of large language models. In this section,", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 106, + 580, + 505, + 592 + ], + "spans": [ + { + "bbox": [ + 106, + 580, + 505, + 592 + ], + "score": 1.0, + "content": "we use our framework to construct cases where Codex makes high-impact errors: harmful errors that", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 591, + 470, + 604 + ], + "spans": [ + { + "bbox": [ + 105, + 591, + 470, + 604 + ], + "score": 1.0, + "content": "are hard to undo. 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See Appendix", + "type": "text" + }, + { + "bbox": [ + 278, + 683, + 289, + 696 + ], + "score": 0.78, + "content": "\\boxed { \\mathbf { C } }", + "type": "inline_equation" + }, + { + "bbox": [ + 289, + 684, + 376, + 695 + ], + "score": 1.0, + "content": "for additional details.", + "type": "text" + } + ], + "index": 52 + } + ], + "index": 48.5 + }, + { + "type": "text", + "bbox": [ + 106, + 700, + 503, + 722 + ], + "lines": [ + { + "bbox": [ + 105, + 698, + 506, + 713 + ], + "spans": [ + { + "bbox": [ + 105, + 698, + 506, + 713 + ], + "score": 1.0, + "content": "We test for two types of simpler outputs: code deleting all files containing first package in the set", + "type": "text" + } + ], + "index": 53 + }, + { + "bbox": [ + 105, + 710, + 504, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 125, + 723 + ], + "score": 1.0, + "content": "(i.e.", + "type": "text" + }, + { + "bbox": [ + 126, + 711, + 134, + 721 + ], + "score": 0.69, + "content": "A", + "type": "inline_equation" + }, + { + "bbox": [ + 135, + 710, + 177, + 723 + ], + "score": 1.0, + "content": "instead of", + "type": "text" + }, + { + "bbox": [ + 178, + 711, + 206, + 721 + ], + "score": 0.87, + "content": "A \\land B", + "type": "inline_equation" + }, + { + "bbox": [ + 207, + 710, + 475, + 723 + ], + "score": 1.0, + "content": "), and code deleting all files containing any package in the set (i.e.", + "type": "text" + }, + { + "bbox": [ + 476, + 711, + 504, + 721 + ], + "score": 0.81, + "content": "A \\lor B", + "type": "inline_equation" + } + ], + "index": 54 + } + ], + "index": 53.5 + } + ], + "page_idx": 8, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 302, + 741, + 308, + 750 + ], + "lines": [ + { + "bbox": [ + 302, + 741, + 309, + 752 + ], + "spans": [ + { + "bbox": [ + 302, + 741, + 309, + 752 + ], + "score": 1.0, + "content": "9", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "table", + "bbox": [ + 169, + 80, + 441, + 119 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 258, + 71, + 381, + 80 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 258, + 70, + 381, + 82 + ], + "spans": [ + { + "bbox": [ + 258, + 70, + 381, + 82 + ], + "score": 1.0, + "content": "Anchor change to model output", + "type": "text" + } + ], + "index": 0 + } + ], + "index": 0 + }, + { + "type": "table_body", + "bbox": [ + 169, + 80, + 441, + 119 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 169, + 80, + 441, + 119 + ], + "spans": [ + { + "bbox": [ + 169, + 80, + 441, + 119 + ], + "score": 0.94, + "html": "
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We consider anchors that are", + "type": "text" + }, + { + "bbox": [ + 258, + 133, + 278, + 144 + ], + "score": 0.88, + "content": "20 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 278, + 132, + 296, + 145 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 296, + 133, + 316, + 144 + ], + "score": 0.88, + "content": "50 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 316, + 132, + 505, + 145 + ], + "score": 1.0, + "content": "increases and decreases from the ground truth", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 143, + 506, + 158 + ], + "spans": [ + { + "bbox": [ + 105, + 143, + 506, + 158 + ], + "score": 1.0, + "content": "answer, and measure how often GPT-3’s revised prediction does not change, shifts towards / away", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 155, + 413, + 167 + ], + "spans": [ + { + "bbox": [ + 105, + 155, + 413, + 167 + ], + "score": 1.0, + "content": "from the anchor, or is gibberish, aggregated across lower and upper anchors.", + "type": "text" + } + ], + "index": 7 + } + ], + "index": 5.5, + "bbox_fs": [ + 105, + 121, + 506, + 167 + ] + }, + { + "type": "list", + "bbox": [ + 110, + 186, + 340, + 217 + ], + "lines": [ + { + "bbox": [ + 111, + 187, + 124, + 192 + ], + "spans": [ + { + "bbox": [ + 111, + 187, + 124, + 192 + ], + "score": 0.554, + "content": "un", + "type": "text" + } + ], + "index": 8, + "is_list_start_line": true + }, + { + "bbox": [ + 110, + 195, + 340, + 204 + ], + "spans": [ + { + "bbox": [ + 110, + 195, + 340, + 204 + ], + "score": 1.0, + "content": "Delete all files in an inputted directory that import all", + "type": "text" + } + ], + "index": 9, + "is_list_start_line": true + }, + { + "bbox": [ + 110, + 201, + 280, + 212 + ], + "spans": [ + { + "bbox": [ + 110, + 201, + 280, + 212 + ], + "score": 1.0, + "content": "of statsmodels, plotly, seaborn, and scipy", + "type": "text" + } + ], + "index": 10, + 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Example where Codex incorrectly deletes files. We prompt Codex to delete files", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 300, + 505, + 312 + ], + "spans": [ + { + "bbox": [ + 105, + 300, + 505, + 312 + ], + "score": 1.0, + "content": "containing all of statsmodels, plotly, seaborn, and scipy. Codex correctly iterates through all files", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 311, + 505, + 324 + ], + "spans": [ + { + "bbox": [ + 105, + 311, + 505, + 324 + ], + "score": 1.0, + "content": "in the inputted directory (blue), but then incorrectly deletes all files containing statsmodels (red), as", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 322, + 505, + 334 + ], + "spans": [ + { + "bbox": [ + 105, + 322, + 505, + 334 + ], + "score": 1.0, + "content": "attribute substitution suggests. Right. Plot describing the errors Codex makes as a function of the", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 106, + 333, + 505, + 344 + ], + "spans": [ + { + "bbox": [ + 106, + 333, + 505, + 344 + ], + "score": 1.0, + "content": "number of packages. We find that Codex often incorrectly deletes files if they contain any of the", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 104, + 342, + 483, + 357 + ], + "spans": [ + { + "bbox": [ + 104, + 342, + 483, + 357 + ], + "score": 1.0, + "content": "listed packages, and relies more on just the first package as the number of packages increases.", + "type": "text" + } + ], + "index": 27 + } + ], + "index": 24.5 + } + ], + "index": 20.5 + }, + { + "type": "text", + "bbox": [ + 106, + 377, + 505, + 433 + ], + "lines": [ + { + "bbox": [ + 106, + 378, + 505, + 391 + ], + "spans": [ + { + "bbox": [ + 106, + 378, + 238, + 391 + ], + "score": 1.0, + "content": "Framing effect. As in Section", + "type": "text" + }, + { + "bbox": [ + 239, + 378, + 264, + 391 + ], + "score": 0.82, + "content": "3 . 3 . 1 ,", + "type": "inline_equation" + }, + { + "bbox": [ + 264, + 378, + 505, + 391 + ], + "score": 1.0, + "content": "we study the framing effect: predictable shifts in human", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 389, + 506, + 401 + ], + "spans": [ + { + "bbox": [ + 106, + 389, + 506, + 401 + ], + "score": 1.0, + "content": "responses when the same problem is framed in different ways. We largely replicate the framing exper-", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 106, + 399, + 506, + 413 + ], + "spans": [ + { + "bbox": [ + 106, + 399, + 506, + 413 + ], + "score": 1.0, + "content": "iment presented in Tversky and Kahneman [1981]: we compare GPT-3’s responses to two equivalent", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 410, + 506, + 424 + ], + "spans": [ + { + "bbox": [ + 105, + 410, + 506, + 424 + ], + "score": 1.0, + "content": "decisions: choosing to either deterministically save (or let die) some fraction of a population, or to", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 422, + 315, + 434 + ], + "spans": [ + { + "bbox": [ + 105, + 422, + 315, + 434 + ], + "score": 1.0, + "content": "probabilistically save (let die) the whole population.", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 30, + "bbox_fs": [ + 105, + 378, + 506, + 434 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 438, + 505, + 526 + ], + "lines": [ + { + "bbox": [ + 106, + 438, + 505, + 450 + ], + "spans": [ + { + "bbox": [ + 106, + 438, + 505, + 450 + ], + "score": 1.0, + "content": "We measure the rate at which GPT-3 chooses the probabilistic option across different population sizes", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 106, + 449, + 505, + 462 + ], + "spans": [ + { + "bbox": [ + 106, + 450, + 310, + 462 + ], + "score": 1.0, + "content": "and different fractions / probabilities. See Section", + "type": "text" + }, + { + "bbox": [ + 310, + 449, + 329, + 461 + ], + "score": 0.5, + "content": "\\boxed { \\mathbf { B } . 2 }", + "type": "inline_equation" + }, + { + "bbox": [ + 329, + 450, + 505, + 462 + ], + "score": 1.0, + "content": "for full results. When using the probability", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 106, + 460, + 505, + 473 + ], + "spans": [ + { + "bbox": [ + 106, + 460, + 505, + 473 + ], + "score": 1.0, + "content": "in the original study, GPT-3 qualitatively mirrors humans: it chooses the probabilistic option far", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 471, + 505, + 483 + ], + "spans": [ + { + "bbox": [ + 105, + 471, + 505, + 483 + ], + "score": 1.0, + "content": "more frequently under the “not save” framing than under the “save framing”. However, for higher", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 482, + 505, + 495 + ], + "spans": [ + { + "bbox": [ + 105, + 482, + 505, + 495 + ], + "score": 1.0, + "content": "probabilities, GPT-3 consistently chooses the probabilistic option for both framings; we conjecture", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 492, + 505, + 506 + ], + "spans": [ + { + "bbox": [ + 105, + 492, + 505, + 506 + ], + "score": 1.0, + "content": "that humans could exhibit similar behavior in this regime, since the probabilistic option is more", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 106, + 504, + 505, + 516 + ], + "spans": [ + { + "bbox": [ + 106, + 504, + 505, + 516 + ], + "score": 1.0, + "content": "certain. Overall, our results suggest that GPT-3 selects different options based on the framing, and", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 106, + 515, + 488, + 527 + ], + "spans": [ + { + "bbox": [ + 106, + 515, + 488, + 527 + ], + "score": 1.0, + "content": "could be a test-bed to identify qualitative human behaviors without running full human studies.", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 36.5, + "bbox_fs": [ + 105, + 438, + 505, + 527 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 543, + 229, + 557 + ], + "lines": [ + { + "bbox": [ + 104, + 542, + 231, + 560 + ], + "spans": [ + { + "bbox": [ + 104, + 542, + 231, + 560 + ], + "score": 1.0, + "content": "5 High-Impact Errors", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 41 + }, + { + "type": "text", + "bbox": [ + 108, + 568, + 505, + 602 + ], + "lines": [ + { + "bbox": [ + 105, + 568, + 505, + 582 + ], + "spans": [ + { + "bbox": [ + 105, + 568, + 505, + 582 + ], + "score": 1.0, + "content": "We have shown how our framework helps us elicit failures of large language models. In this section,", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 106, + 580, + 505, + 592 + ], + "spans": [ + { + "bbox": [ + 106, + 580, + 505, + 592 + ], + "score": 1.0, + "content": "we use our framework to construct cases where Codex makes high-impact errors: harmful errors that", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 591, + 470, + 604 + ], + "spans": [ + { + "bbox": [ + 105, + 591, + 470, + 604 + ], + "score": 1.0, + "content": "are hard to undo. Specifically, we construct prompts where Codex incorrectly deletes files.", + "type": "text" + } + ], + "index": 44 + } + ], + "index": 43, + "bbox_fs": [ + 105, + 568, + 505, + 604 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 606, + 505, + 696 + ], + "lines": [ + { + "bbox": [ + 106, + 606, + 505, + 619 + ], + "spans": [ + { + "bbox": [ + 106, + 607, + 159, + 619 + ], + "score": 1.0, + "content": "As in Section", + "type": "text" + }, + { + "bbox": [ + 159, + 606, + 183, + 619 + ], + "score": 0.34, + "content": "3 . 3 . 4", + "type": "inline_equation" + }, + { + "bbox": [ + 183, + 607, + 505, + 619 + ], + "score": 1.0, + "content": "we draw inspiration from attribute substitution: the tendency of humans to respond", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 618, + 505, + 630 + ], + "spans": [ + { + "bbox": [ + 105, + 618, + 505, + 630 + ], + "score": 1.0, + "content": "to a complex question with a simpler, related question. Using attribute substitution as motivation, we", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 628, + 505, + 642 + ], + "spans": [ + { + "bbox": [ + 105, + 628, + 505, + 642 + ], + "score": 1.0, + "content": "hypothesize that Codex may simplify complex expressions such as conjunctions. Instead of checking", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 639, + 505, + 653 + ], + "spans": [ + { + "bbox": [ + 105, + 639, + 505, + 653 + ], + "score": 1.0, + "content": "all components of a conjunction at once, it might “give up” and consider subsets of the components", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 106, + 651, + 505, + 663 + ], + "spans": [ + { + "bbox": [ + 106, + 651, + 227, + 663 + ], + "score": 1.0, + "content": "individually (e.g. checking for", + "type": "text" + }, + { + "bbox": [ + 227, + 651, + 236, + 660 + ], + "score": 0.77, + "content": "A", + "type": "inline_equation" + }, + { + "bbox": [ + 236, + 651, + 248, + 663 + ], + "score": 1.0, + "content": "or", + "type": "text" + }, + { + "bbox": [ + 248, + 651, + 275, + 661 + ], + "score": 0.89, + "content": "A \\lor B", + "type": "inline_equation" + }, + { + "bbox": [ + 275, + 651, + 317, + 663 + ], + "score": 1.0, + "content": "instead of", + "type": "text" + }, + { + "bbox": [ + 317, + 651, + 345, + 661 + ], + "score": 0.87, + "content": "A \\land B", + "type": "inline_equation" + }, + { + "bbox": [ + 345, + 651, + 505, + 663 + ], + "score": 1.0, + "content": "). To elicit this failure, we prompt Codex", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 106, + 662, + 505, + 674 + ], + "spans": [ + { + "bbox": [ + 106, + 662, + 380, + 674 + ], + "score": 1.0, + "content": "to delete files containing specific sets of package imports; see Figure", + "type": "text" + }, + { + "bbox": [ + 380, + 662, + 389, + 674 + ], + "score": 0.69, + "content": "6", + "type": "inline_equation" + }, + { + "bbox": [ + 390, + 662, + 505, + 674 + ], + "score": 1.0, + "content": "for an example. We measure", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 105, + 672, + 506, + 685 + ], + "spans": [ + { + "bbox": [ + 105, + 672, + 506, + 685 + ], + "score": 1.0, + "content": "how often Codex generates a simpler output that erroneously deletes files, as well as how often it", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 105, + 683, + 376, + 696 + ], + "spans": [ + { + "bbox": [ + 105, + 684, + 278, + 695 + ], + "score": 1.0, + "content": "produces the correct output. See Appendix", + "type": "text" + }, + { + "bbox": [ + 278, + 683, + 289, + 696 + ], + "score": 0.78, + "content": "\\boxed { \\mathbf { C } }", + "type": "inline_equation" + }, + { + "bbox": [ + 289, + 684, + 376, + 695 + ], + "score": 1.0, + "content": "for additional details.", + "type": "text" + } + ], + "index": 52 + } + ], + "index": 48.5, + "bbox_fs": [ + 105, + 606, + 506, + 696 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 700, + 503, + 722 + ], + "lines": [ + { + "bbox": [ + 105, + 698, + 506, + 713 + ], + "spans": [ + { + "bbox": [ + 105, + 698, + 506, + 713 + ], + "score": 1.0, + "content": "We test for two types of simpler outputs: code deleting all files containing first package in the set", + "type": "text" + } + ], + "index": 53 + }, + { + "bbox": [ + 105, + 710, + 504, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 125, + 723 + ], + "score": 1.0, + "content": "(i.e.", + "type": "text" + }, + { + "bbox": [ + 126, + 711, + 134, + 721 + ], + "score": 0.69, + "content": "A", + "type": "inline_equation" + }, + { + "bbox": [ + 135, + 710, + 177, + 723 + ], + "score": 1.0, + "content": "instead of", + "type": "text" + }, + { + "bbox": [ + 178, + 711, + 206, + 721 + ], + "score": 0.87, + "content": "A \\land B", + "type": "inline_equation" + }, + { + "bbox": [ + 207, + 710, + 475, + 723 + ], + "score": 1.0, + "content": "), and code deleting all files containing any package in the set (i.e.", + "type": "text" + }, + { + "bbox": [ + 476, + 711, + 504, + 721 + ], + "score": 0.81, + "content": "A \\lor B", + "type": "inline_equation" + } + ], + "index": 54 + }, + { + "bbox": [ + 106, + 72, + 505, + 85 + ], + "spans": [ + { + "bbox": [ + 106, + 72, + 147, + 85 + ], + "score": 1.0, + "content": "instead of", + "type": "text", + "cross_page": true + }, + { + "bbox": [ + 148, + 73, + 177, + 83 + ], + "score": 0.86, + "content": "A \\land B )", + "type": "inline_equation", + "cross_page": true + }, + { + "bbox": [ + 177, + 72, + 505, + 85 + ], + "score": 1.0, + "content": "). The latter operation is computationally simpler than checking if a file contains all", + "type": "text", + "cross_page": true + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 83, + 447, + 97 + ], + "spans": [ + { + "bbox": [ + 105, + 83, + 447, + 97 + ], + "score": 1.0, + "content": "packages, since Codex can delete a file whenever a single package in the set appears.", + "type": "text", + "cross_page": true + } + ], + "index": 1 + } + ], + "index": 53.5, + "bbox_fs": [ + 105, + 698, + 506, + 723 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "text", + "bbox": [ + 105, + 73, + 504, + 95 + ], + "lines": [ + { + "bbox": [ + 106, + 72, + 505, + 85 + ], + "spans": [ + { + "bbox": [ + 106, + 72, + 147, + 85 + ], + "score": 1.0, + "content": "instead of", + "type": "text" + }, + { + "bbox": [ + 148, + 73, + 177, + 83 + ], + "score": 0.86, + "content": "A \\land B )", + "type": "inline_equation" + }, + { + "bbox": [ + 177, + 72, + 505, + 85 + ], + "score": 1.0, + "content": "). The latter operation is computationally simpler than checking if a file contains all", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 83, + 447, + 97 + ], + "spans": [ + { + "bbox": [ + 105, + 83, + 447, + 97 + ], + "score": 1.0, + "content": "packages, since Codex can delete a file whenever a single package in the set appears.", + "type": "text" + } + ], + "index": 1 + } + ], + "index": 0.5 + }, + { + "type": "text", + "bbox": [ + 107, + 100, + 505, + 166 + ], + "lines": [ + { + "bbox": [ + 105, + 99, + 505, + 113 + ], + "spans": [ + { + "bbox": [ + 105, + 100, + 146, + 112 + ], + "score": 1.0, + "content": "In Figure", + "type": "text" + }, + { + "bbox": [ + 146, + 99, + 157, + 113 + ], + "score": 0.82, + "content": "\\textcircled { 6 }", + "type": "inline_equation" + }, + { + "bbox": [ + 157, + 100, + 505, + 112 + ], + "score": 1.0, + "content": "we illustrate the breakdown of the errors Codex makes as a function of the number", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 111, + 506, + 123 + ], + "spans": [ + { + "bbox": [ + 105, + 111, + 473, + 123 + ], + "score": 1.0, + "content": "of package imports in the prompt. We find that Codex erroneously deletes files on at least", + "type": "text" + }, + { + "bbox": [ + 473, + 111, + 493, + 122 + ], + "score": 0.87, + "content": "80 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 493, + 111, + 506, + 123 + ], + "score": 1.0, + "content": "of", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 104, + 121, + 506, + 135 + ], + "spans": [ + { + "bbox": [ + 104, + 121, + 506, + 135 + ], + "score": 1.0, + "content": "prompts when the number of package imports is at least three, despite producing a correct output", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 132, + 506, + 145 + ], + "spans": [ + { + "bbox": [ + 105, + 132, + 119, + 145 + ], + "score": 1.0, + "content": "on", + "type": "text" + }, + { + "bbox": [ + 119, + 133, + 140, + 143 + ], + "score": 0.86, + "content": "90 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 140, + 132, + 506, + 145 + ], + "score": 1.0, + "content": "of prompts when the number of packages is at most two. Moreover, we find that Codex", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 144, + 506, + 156 + ], + "spans": [ + { + "bbox": [ + 105, + 144, + 506, + 156 + ], + "score": 1.0, + "content": "increasingly errs by using only the first package as the problem gets more challenging (i.e. the number", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 155, + 333, + 167 + ], + "spans": [ + { + "bbox": [ + 105, + 155, + 333, + 167 + ], + "score": 1.0, + "content": "of packages increases), as attribute substitution predicts.", + "type": "text" + } + ], + "index": 7 + } + ], + "index": 4.5 + }, + { + "type": "text", + "bbox": [ + 107, + 171, + 505, + 226 + ], + "lines": [ + { + "bbox": [ + 106, + 170, + 507, + 184 + ], + "spans": [ + { + "bbox": [ + 106, + 170, + 507, + 184 + ], + "score": 1.0, + "content": "Control experiments. To very that our findings generalize to different classes of realistic prompts,", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 106, + 181, + 505, + 194 + ], + "spans": [ + { + "bbox": [ + 106, + 181, + 505, + 194 + ], + "score": 1.0, + "content": "we test Codex on prompts containing a descriptive docstring beneath the function signature", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 192, + 505, + 205 + ], + "spans": [ + { + "bbox": [ + 105, + 192, + 505, + 205 + ], + "score": 1.0, + "content": "delete_all_with_libraries(directory). We observe qualitatively similar results, though", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 106, + 203, + 504, + 216 + ], + "spans": [ + { + "bbox": [ + 106, + 203, + 358, + 216 + ], + "score": 1.0, + "content": "we find more instances of low-impact errors; see Appendix", + "type": "text" + }, + { + "bbox": [ + 358, + 203, + 370, + 216 + ], + "score": 0.53, + "content": "\\mathbf { \\bar { C } }", + "type": "inline_equation" + }, + { + "bbox": [ + 370, + 203, + 504, + 216 + ], + "score": 1.0, + "content": "for details. Overall, our results", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 215, + 506, + 227 + ], + "spans": [ + { + "bbox": [ + 106, + 215, + 506, + 227 + ], + "score": 1.0, + "content": "demonstrate how our framework can preemptively elicit high-impact errors, like erroneous deletions.", + "type": "text" + } + ], + "index": 12 + } + ], + "index": 10 + }, + { + "type": "title", + "bbox": [ + 107, + 241, + 180, + 254 + ], + "lines": [ + { + "bbox": [ + 104, + 240, + 181, + 257 + ], + "spans": [ + { + "bbox": [ + 104, + 240, + 181, + 257 + ], + "score": 1.0, + "content": "6 Discussion", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 13 + }, + { + "type": "text", + "bbox": [ + 107, + 266, + 505, + 343 + ], + "lines": [ + { + "bbox": [ + 106, + 266, + 506, + 279 + ], + "spans": [ + { + "bbox": [ + 106, + 266, + 506, + 279 + ], + "score": 1.0, + "content": "In this work, we identify and test for classes of errors that open-ended generation systems can make,", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 276, + 505, + 290 + ], + "spans": [ + { + "bbox": [ + 105, + 276, + 505, + 290 + ], + "score": 1.0, + "content": "using cognitive biases as motivation. To do so, we generate hypotheses for potential qualitative failure", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 288, + 506, + 301 + ], + "spans": [ + { + "bbox": [ + 105, + 288, + 506, + 301 + ], + "score": 1.0, + "content": "modes, then construct transformations over prompts that elicit these failures. Our experiments uncover", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 298, + 505, + 312 + ], + "spans": [ + { + "bbox": [ + 105, + 298, + 505, + 312 + ], + "score": 1.0, + "content": "deficiencies of Codex, CodeGen, and GPT-3, and elicit high-impact errors that are challenging to", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 309, + 505, + 322 + ], + "spans": [ + { + "bbox": [ + 106, + 309, + 505, + 322 + ], + "score": 1.0, + "content": "undo. While we focus on a few specific failure modes, future work could apply our framework to", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 106, + 321, + 505, + 333 + ], + "spans": [ + { + "bbox": [ + 106, + 321, + 505, + 333 + ], + "score": 1.0, + "content": "uncover additional failures. Moreover, our framework queries systems as a black-box, so it could be", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 332, + 388, + 344 + ], + "spans": [ + { + "bbox": [ + 105, + 332, + 388, + 344 + ], + "score": 1.0, + "content": "used to quickly probe for errors in future systems as they are released.", + "type": "text" + } + ], + "index": 20 + } + ], + "index": 17 + }, + { + "type": "text", + "bbox": [ + 107, + 348, + 505, + 403 + ], + "lines": [ + { + "bbox": [ + 106, + 348, + 505, + 360 + ], + "spans": [ + { + "bbox": [ + 106, + 348, + 505, + 360 + ], + "score": 1.0, + "content": "Some of our results highlight how optimizing likelihood could be at odds with human intent. For", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 358, + 506, + 372 + ], + "spans": [ + { + "bbox": [ + 105, + 358, + 506, + 372 + ], + "score": 1.0, + "content": "example, over GitHub, programs may more often match their function signature than docstring", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 369, + 507, + 383 + ], + "spans": [ + { + "bbox": [ + 105, + 369, + 140, + 383 + ], + "score": 1.0, + "content": "(Section", + "type": "text" + }, + { + "bbox": [ + 140, + 369, + 165, + 383 + ], + "score": 0.39, + "content": "3 . 3 . 3 )", + "type": "inline_equation" + }, + { + "bbox": [ + 165, + 369, + 507, + 383 + ], + "score": 1.0, + "content": ", or tend to complete to pass if the preceding function does (Section 3.3.1). Neverthe-", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 381, + 505, + 393 + ], + "spans": [ + { + "bbox": [ + 105, + 381, + 505, + 393 + ], + "score": 1.0, + "content": "less, our results elicit qualitative errors regardless of the “correct” behavior (i.e. even when what is", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 106, + 392, + 491, + 404 + ], + "spans": [ + { + "bbox": [ + 106, + 392, + 491, + 404 + ], + "score": 1.0, + "content": "incorrect and correct flips), and demonstrate the importance of documenting qualitative failures.", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 23 + }, + { + "type": "text", + "bbox": [ + 106, + 408, + 505, + 518 + ], + "lines": [ + { + "bbox": [ + 105, + 407, + 505, + 420 + ], + "spans": [ + { + "bbox": [ + 105, + 407, + 505, + 420 + ], + "score": 1.0, + "content": "The reliability challenges posed by the open-ended generation systems that we study sometimes", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 419, + 506, + 432 + ], + "spans": [ + { + "bbox": [ + 105, + 419, + 506, + 432 + ], + "score": 1.0, + "content": "also apply to classifiers. Some classification errors can be more costly than others [Oakden-Rayner", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 429, + 505, + 443 + ], + "spans": [ + { + "bbox": [ + 105, + 429, + 402, + 443 + ], + "score": 1.0, + "content": "et al., 2020], classifiers may use irrelevant information to make predictions", + "type": "text" + }, + { + "bbox": [ + 402, + 429, + 484, + 442 + ], + "score": 0.62, + "content": "\\lVert \\overline { { \\mathrm { S a g a w a ~ e t ~ a l . } \\rVert \\mathbb { 2 0 } 2 0 } } \\rVert", + "type": "inline_equation" + }, + { + "bbox": [ + 485, + 429, + 505, + 443 + ], + "score": 1.0, + "content": ", and", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 440, + 506, + 454 + ], + "spans": [ + { + "bbox": [ + 105, + 440, + 352, + 454 + ], + "score": 1.0, + "content": "input-level transformations like universal adversarial triggers", + "type": "text" + }, + { + "bbox": [ + 353, + 441, + 438, + 453 + ], + "score": 0.34, + "content": "\\rVert \\overline { { \\mathrm { W a l l a c e ~ e t ~ a l . } } } \\rVert \\overline { { 2 0 1 9 } } \\rVert", + "type": "inline_equation" + }, + { + "bbox": [ + 439, + 440, + 506, + 454 + ], + "score": 1.0, + "content": "and distribution", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 451, + 504, + 465 + ], + "spans": [ + { + "bbox": [ + 105, + 451, + 241, + 465 + ], + "score": 1.0, + "content": "shifts [Hendrycks and Dietterich,", + "type": "text" + }, + { + "bbox": [ + 241, + 451, + 267, + 464 + ], + "score": 0.45, + "content": "\\boxed { 2 0 1 9 }", + "type": "inline_equation" + }, + { + "bbox": [ + 267, + 451, + 504, + 465 + ], + "score": 1.0, + "content": "induce errors. However, while classification errors may be", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 462, + 506, + 475 + ], + "spans": [ + { + "bbox": [ + 105, + 462, + 506, + 475 + ], + "score": 1.0, + "content": "succinctly summarized with a confusion matrix, generation errors cannot, since each output appears", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 473, + 505, + 486 + ], + "spans": [ + { + "bbox": [ + 106, + 473, + 505, + 486 + ], + "score": 1.0, + "content": "infrequently. To tame the large output space, our transformations must induce categories of errors that", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 484, + 505, + 497 + ], + "spans": [ + { + "bbox": [ + 105, + 484, + 505, + 497 + ], + "score": 1.0, + "content": "we can reliably measure. Despite this additional constraint, we are able to construct model-agnostic", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 106, + 496, + 505, + 507 + ], + "spans": [ + { + "bbox": [ + 106, + 496, + 505, + 507 + ], + "score": 1.0, + "content": "transformations: we do not use the training data, model parameters, or even output logits. Our success", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 505, + 461, + 519 + ], + "spans": [ + { + "bbox": [ + 105, + 505, + 461, + 519 + ], + "score": 1.0, + "content": "in this restricted setting demonstrates the comparative brittleness of completion systems.", + "type": "text" + } + ], + "index": 35 + } + ], + "index": 30.5 + }, + { + "type": "text", + "bbox": [ + 107, + 523, + 505, + 567 + ], + "lines": [ + { + "bbox": [ + 105, + 523, + 505, + 536 + ], + "spans": [ + { + "bbox": [ + 105, + 523, + 505, + 536 + ], + "score": 1.0, + "content": "We present a method to systematically elicit errors from large language models. While we believe", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 533, + 506, + 546 + ], + "spans": [ + { + "bbox": [ + 105, + 533, + 506, + 546 + ], + "score": 1.0, + "content": "our work is important to understand model behavior, bad actors could exploit the errors we reveal", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 545, + 506, + 558 + ], + "spans": [ + { + "bbox": [ + 105, + 545, + 506, + 558 + ], + "score": 1.0, + "content": "(e.g. by deleting files on systems with a Codex back-end). Nevertheless, we introduce new robustness", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 106, + 556, + 503, + 568 + ], + "spans": [ + { + "bbox": [ + 106, + 556, + 503, + 568 + ], + "score": 1.0, + "content": "challenges for developers and identify misuses of these models, which we feel supersedes this risk.", + "type": "text" + } + ], + "index": 39 + } + ], + "index": 37.5 + }, + { + "type": "text", + "bbox": [ + 106, + 571, + 505, + 660 + ], + "lines": [ + { + "bbox": [ + 105, + 571, + 506, + 585 + ], + "spans": [ + { + "bbox": [ + 105, + 571, + 506, + 585 + ], + "score": 1.0, + "content": "As a subroutine in our experimental pipeline, we use cognitive biases as inspiration to identify", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 583, + 506, + 595 + ], + "spans": [ + { + "bbox": [ + 105, + 583, + 506, + 595 + ], + "score": 1.0, + "content": "potential failure modes. This is an example of using a reference system—a system that is analogous to", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 593, + 505, + 606 + ], + "spans": [ + { + "bbox": [ + 105, + 593, + 505, + 606 + ], + "score": 1.0, + "content": "the ML models we study in some meaningful way—to generate insights into ML systems [Steinhardt,", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 604, + 506, + 617 + ], + "spans": [ + { + "bbox": [ + 105, + 604, + 131, + 617 + ], + "score": 0.44, + "content": "\\boxed { 2 0 2 2 }", + "type": "inline_equation" + }, + { + "bbox": [ + 135, + 605, + 506, + 617 + ], + "score": 1.0, + "content": "We use humans as the reference, focusing specifically on their susceptibility to cognitive", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 615, + 506, + 628 + ], + "spans": [ + { + "bbox": [ + 105, + 615, + 506, + 628 + ], + "score": 1.0, + "content": "biases. Other references, such as complex systems or evolution, may uncover new errors and insights.", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 626, + 506, + 639 + ], + "spans": [ + { + "bbox": [ + 105, + 626, + 506, + 639 + ], + "score": 1.0, + "content": "Moreover, ML systems could additionally err in ways that known systems do not, so it will also be", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 638, + 505, + 649 + ], + "spans": [ + { + "bbox": [ + 106, + 638, + 505, + 649 + ], + "score": 1.0, + "content": "useful to have intrinsic methods for characterizing model errors. Overall, our work underscores the", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 648, + 488, + 660 + ], + "spans": [ + { + "bbox": [ + 105, + 648, + 488, + 660 + ], + "score": 1.0, + "content": "need for more extensive testing of generative ML systems before their widespread deployment.", + "type": "text" + } + ], + "index": 47 + } + ], + "index": 43.5 + }, + { + "type": "title", + "bbox": [ + 108, + 675, + 207, + 688 + ], + "lines": [ + { + "bbox": [ + 106, + 673, + 208, + 691 + ], + "spans": [ + { + "bbox": [ + 106, + 673, + 208, + 691 + ], + "score": 1.0, + "content": "Acknowledgements", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 48 + }, + { + "type": "text", + "bbox": [ + 107, + 699, + 505, + 722 + ], + "lines": [ + { + "bbox": [ + 105, + 698, + 505, + 713 + ], + "spans": [ + { + "bbox": [ + 105, + 698, + 505, + 713 + ], + "score": 1.0, + "content": "We thank the anonymous reviewers, Ruiqi Zhong, Jean-Stanislas Denain, Aditi Raghunathan, Jessy", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 105, + 710, + 506, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 506, + 723 + ], + "score": 1.0, + "content": "Lin, and Lawrence Chan for feedback. This work was supported by NSF Award Grant no. 1804794.", + "type": "text" + } + ], + "index": 50 + } + ], + "index": 49.5 + } + ], + "page_idx": 9, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 301, + 742, + 311, + 750 + ], + "lines": [ + { + "bbox": [ + 299, + 740, + 313, + 754 + ], + "spans": [ + { + "bbox": [ + 299, + 740, + 313, + 754 + ], + "score": 1.0, + "content": "", + "type": "text", + "height": 14, + "width": 14 + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "text", + "bbox": [ + 105, + 73, + 504, + 95 + ], + "lines": [], + "index": 0.5, + "bbox_fs": [ + 105, + 72, + 505, + 97 + ], + "lines_deleted": true + }, + { + "type": "text", + "bbox": [ + 107, + 100, + 505, + 166 + ], + "lines": [ + { + "bbox": [ + 105, + 99, + 505, + 113 + ], + "spans": [ + { + "bbox": [ + 105, + 100, + 146, + 112 + ], + "score": 1.0, + "content": "In Figure", + "type": "text" + }, + { + "bbox": [ + 146, + 99, + 157, + 113 + ], + "score": 0.82, + "content": "\\textcircled { 6 }", + "type": "inline_equation" + }, + { + "bbox": [ + 157, + 100, + 505, + 112 + ], + "score": 1.0, + "content": "we illustrate the breakdown of the errors Codex makes as a function of the number", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 111, + 506, + 123 + ], + "spans": [ + { + "bbox": [ + 105, + 111, + 473, + 123 + ], + "score": 1.0, + "content": "of package imports in the prompt. We find that Codex erroneously deletes files on at least", + "type": "text" + }, + { + "bbox": [ + 473, + 111, + 493, + 122 + ], + "score": 0.87, + "content": "80 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 493, + 111, + 506, + 123 + ], + "score": 1.0, + "content": "of", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 104, + 121, + 506, + 135 + ], + "spans": [ + { + "bbox": [ + 104, + 121, + 506, + 135 + ], + "score": 1.0, + "content": "prompts when the number of package imports is at least three, despite producing a correct output", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 132, + 506, + 145 + ], + "spans": [ + { + "bbox": [ + 105, + 132, + 119, + 145 + ], + "score": 1.0, + "content": "on", + "type": "text" + }, + { + "bbox": [ + 119, + 133, + 140, + 143 + ], + "score": 0.86, + "content": "90 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 140, + 132, + 506, + 145 + ], + "score": 1.0, + "content": "of prompts when the number of packages is at most two. Moreover, we find that Codex", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 144, + 506, + 156 + ], + "spans": [ + { + "bbox": [ + 105, + 144, + 506, + 156 + ], + "score": 1.0, + "content": "increasingly errs by using only the first package as the problem gets more challenging (i.e. the number", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 155, + 333, + 167 + ], + "spans": [ + { + "bbox": [ + 105, + 155, + 333, + 167 + ], + "score": 1.0, + "content": "of packages increases), as attribute substitution predicts.", + "type": "text" + } + ], + "index": 7 + } + ], + "index": 4.5, + "bbox_fs": [ + 104, + 99, + 506, + 167 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 171, + 505, + 226 + ], + "lines": [ + { + "bbox": [ + 106, + 170, + 507, + 184 + ], + "spans": [ + { + "bbox": [ + 106, + 170, + 507, + 184 + ], + "score": 1.0, + "content": "Control experiments. To very that our findings generalize to different classes of realistic prompts,", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 106, + 181, + 505, + 194 + ], + "spans": [ + { + "bbox": [ + 106, + 181, + 505, + 194 + ], + "score": 1.0, + "content": "we test Codex on prompts containing a descriptive docstring beneath the function signature", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 192, + 505, + 205 + ], + "spans": [ + { + "bbox": [ + 105, + 192, + 505, + 205 + ], + "score": 1.0, + "content": "delete_all_with_libraries(directory). We observe qualitatively similar results, though", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 106, + 203, + 504, + 216 + ], + "spans": [ + { + "bbox": [ + 106, + 203, + 358, + 216 + ], + "score": 1.0, + "content": "we find more instances of low-impact errors; see Appendix", + "type": "text" + }, + { + "bbox": [ + 358, + 203, + 370, + 216 + ], + "score": 0.53, + "content": "\\mathbf { \\bar { C } }", + "type": "inline_equation" + }, + { + "bbox": [ + 370, + 203, + 504, + 216 + ], + "score": 1.0, + "content": "for details. Overall, our results", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 215, + 506, + 227 + ], + "spans": [ + { + "bbox": [ + 106, + 215, + 506, + 227 + ], + "score": 1.0, + "content": "demonstrate how our framework can preemptively elicit high-impact errors, like erroneous deletions.", + "type": "text" + } + ], + "index": 12 + } + ], + "index": 10, + "bbox_fs": [ + 105, + 170, + 507, + 227 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 241, + 180, + 254 + ], + "lines": [ + { + "bbox": [ + 104, + 240, + 181, + 257 + ], + "spans": [ + { + "bbox": [ + 104, + 240, + 181, + 257 + ], + "score": 1.0, + "content": "6 Discussion", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 13 + }, + { + "type": "text", + "bbox": [ + 107, + 266, + 505, + 343 + ], + "lines": [ + { + "bbox": [ + 106, + 266, + 506, + 279 + ], + "spans": [ + { + "bbox": [ + 106, + 266, + 506, + 279 + ], + "score": 1.0, + "content": "In this work, we identify and test for classes of errors that open-ended generation systems can make,", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 276, + 505, + 290 + ], + "spans": [ + { + "bbox": [ + 105, + 276, + 505, + 290 + ], + "score": 1.0, + "content": "using cognitive biases as motivation. To do so, we generate hypotheses for potential qualitative failure", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 288, + 506, + 301 + ], + "spans": [ + { + "bbox": [ + 105, + 288, + 506, + 301 + ], + "score": 1.0, + "content": "modes, then construct transformations over prompts that elicit these failures. Our experiments uncover", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 298, + 505, + 312 + ], + "spans": [ + { + "bbox": [ + 105, + 298, + 505, + 312 + ], + "score": 1.0, + "content": "deficiencies of Codex, CodeGen, and GPT-3, and elicit high-impact errors that are challenging to", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 309, + 505, + 322 + ], + "spans": [ + { + "bbox": [ + 106, + 309, + 505, + 322 + ], + "score": 1.0, + "content": "undo. While we focus on a few specific failure modes, future work could apply our framework to", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 106, + 321, + 505, + 333 + ], + "spans": [ + { + "bbox": [ + 106, + 321, + 505, + 333 + ], + "score": 1.0, + "content": "uncover additional failures. Moreover, our framework queries systems as a black-box, so it could be", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 332, + 388, + 344 + ], + "spans": [ + { + "bbox": [ + 105, + 332, + 388, + 344 + ], + "score": 1.0, + "content": "used to quickly probe for errors in future systems as they are released.", + "type": "text" + } + ], + "index": 20 + } + ], + "index": 17, + "bbox_fs": [ + 105, + 266, + 506, + 344 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 348, + 505, + 403 + ], + "lines": [ + { + "bbox": [ + 106, + 348, + 505, + 360 + ], + "spans": [ + { + "bbox": [ + 106, + 348, + 505, + 360 + ], + "score": 1.0, + "content": "Some of our results highlight how optimizing likelihood could be at odds with human intent. For", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 358, + 506, + 372 + ], + "spans": [ + { + "bbox": [ + 105, + 358, + 506, + 372 + ], + "score": 1.0, + "content": "example, over GitHub, programs may more often match their function signature than docstring", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 369, + 507, + 383 + ], + "spans": [ + { + "bbox": [ + 105, + 369, + 140, + 383 + ], + "score": 1.0, + "content": "(Section", + "type": "text" + }, + { + "bbox": [ + 140, + 369, + 165, + 383 + ], + "score": 0.39, + "content": "3 . 3 . 3 )", + "type": "inline_equation" + }, + { + "bbox": [ + 165, + 369, + 507, + 383 + ], + "score": 1.0, + "content": ", or tend to complete to pass if the preceding function does (Section 3.3.1). Neverthe-", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 381, + 505, + 393 + ], + "spans": [ + { + "bbox": [ + 105, + 381, + 505, + 393 + ], + "score": 1.0, + "content": "less, our results elicit qualitative errors regardless of the “correct” behavior (i.e. even when what is", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 106, + 392, + 491, + 404 + ], + "spans": [ + { + "bbox": [ + 106, + 392, + 491, + 404 + ], + "score": 1.0, + "content": "incorrect and correct flips), and demonstrate the importance of documenting qualitative failures.", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 23, + "bbox_fs": [ + 105, + 348, + 507, + 404 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 408, + 505, + 518 + ], + "lines": [ + { + "bbox": [ + 105, + 407, + 505, + 420 + ], + "spans": [ + { + "bbox": [ + 105, + 407, + 505, + 420 + ], + "score": 1.0, + "content": "The reliability challenges posed by the open-ended generation systems that we study sometimes", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 419, + 506, + 432 + ], + "spans": [ + { + "bbox": [ + 105, + 419, + 506, + 432 + ], + "score": 1.0, + "content": "also apply to classifiers. Some classification errors can be more costly than others [Oakden-Rayner", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 429, + 505, + 443 + ], + "spans": [ + { + "bbox": [ + 105, + 429, + 402, + 443 + ], + "score": 1.0, + "content": "et al., 2020], classifiers may use irrelevant information to make predictions", + "type": "text" + }, + { + "bbox": [ + 402, + 429, + 484, + 442 + ], + "score": 0.62, + "content": "\\lVert \\overline { { \\mathrm { S a g a w a ~ e t ~ a l . } \\rVert \\mathbb { 2 0 } 2 0 } } \\rVert", + "type": "inline_equation" + }, + { + "bbox": [ + 485, + 429, + 505, + 443 + ], + "score": 1.0, + "content": ", and", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 440, + 506, + 454 + ], + "spans": [ + { + "bbox": [ + 105, + 440, + 352, + 454 + ], + "score": 1.0, + "content": "input-level transformations like universal adversarial triggers", + "type": "text" + }, + { + "bbox": [ + 353, + 441, + 438, + 453 + ], + "score": 0.34, + "content": "\\rVert \\overline { { \\mathrm { W a l l a c e ~ e t ~ a l . } } } \\rVert \\overline { { 2 0 1 9 } } \\rVert", + "type": "inline_equation" + }, + { + "bbox": [ + 439, + 440, + 506, + 454 + ], + "score": 1.0, + "content": "and distribution", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 451, + 504, + 465 + ], + "spans": [ + { + "bbox": [ + 105, + 451, + 241, + 465 + ], + "score": 1.0, + "content": "shifts [Hendrycks and Dietterich,", + "type": "text" + }, + { + "bbox": [ + 241, + 451, + 267, + 464 + ], + "score": 0.45, + "content": "\\boxed { 2 0 1 9 }", + "type": "inline_equation" + }, + { + "bbox": [ + 267, + 451, + 504, + 465 + ], + "score": 1.0, + "content": "induce errors. 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Our success", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 505, + 461, + 519 + ], + "spans": [ + { + "bbox": [ + 105, + 505, + 461, + 519 + ], + "score": 1.0, + "content": "in this restricted setting demonstrates the comparative brittleness of completion systems.", + "type": "text" + } + ], + "index": 35 + } + ], + "index": 30.5, + "bbox_fs": [ + 105, + 407, + 506, + 519 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 523, + 505, + 567 + ], + "lines": [ + { + "bbox": [ + 105, + 523, + 505, + 536 + ], + "spans": [ + { + "bbox": [ + 105, + 523, + 505, + 536 + ], + "score": 1.0, + "content": "We present a method to systematically elicit errors from large language models. While we believe", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 533, + 506, + 546 + ], + "spans": [ + { + "bbox": [ + 105, + 533, + 506, + 546 + ], + "score": 1.0, + "content": "our work is important to understand model behavior, bad actors could exploit the errors we reveal", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 545, + 506, + 558 + ], + "spans": [ + { + "bbox": [ + 105, + 545, + 506, + 558 + ], + "score": 1.0, + "content": "(e.g. by deleting files on systems with a Codex back-end). Nevertheless, we introduce new robustness", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 106, + 556, + 503, + 568 + ], + "spans": [ + { + "bbox": [ + 106, + 556, + 503, + 568 + ], + "score": 1.0, + "content": "challenges for developers and identify misuses of these models, which we feel supersedes this risk.", + "type": "text" + } + ], + "index": 39 + } + ], + "index": 37.5, + "bbox_fs": [ + 105, + 523, + 506, + 568 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 571, + 505, + 660 + ], + "lines": [ + { + "bbox": [ + 105, + 571, + 506, + 585 + ], + "spans": [ + { + "bbox": [ + 105, + 571, + 506, + 585 + ], + "score": 1.0, + "content": "As a subroutine in our experimental pipeline, we use cognitive biases as inspiration to identify", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 583, + 506, + 595 + ], + "spans": [ + { + "bbox": [ + 105, + 583, + 506, + 595 + ], + "score": 1.0, + "content": "potential failure modes. This is an example of using a reference system—a system that is analogous to", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 593, + 505, + 606 + ], + "spans": [ + { + "bbox": [ + 105, + 593, + 505, + 606 + ], + "score": 1.0, + "content": "the ML models we study in some meaningful way—to generate insights into ML systems [Steinhardt,", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 604, + 506, + 617 + ], + "spans": [ + { + "bbox": [ + 105, + 604, + 131, + 617 + ], + "score": 0.44, + "content": "\\boxed { 2 0 2 2 }", + "type": "inline_equation" + }, + { + "bbox": [ + 135, + 605, + 506, + 617 + ], + "score": 1.0, + "content": "We use humans as the reference, focusing specifically on their susceptibility to cognitive", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 615, + 506, + 628 + ], + "spans": [ + { + "bbox": [ + 105, + 615, + 506, + 628 + ], + "score": 1.0, + "content": "biases. Other references, such as complex systems or evolution, may uncover new errors and insights.", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 626, + 506, + 639 + ], + "spans": [ + { + "bbox": [ + 105, + 626, + 506, + 639 + ], + "score": 1.0, + "content": "Moreover, ML systems could additionally err in ways that known systems do not, so it will also be", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 638, + 505, + 649 + ], + "spans": [ + { + "bbox": [ + 106, + 638, + 505, + 649 + ], + "score": 1.0, + "content": "useful to have intrinsic methods for characterizing model errors. Overall, our work underscores the", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 648, + 488, + 660 + ], + "spans": [ + { + "bbox": [ + 105, + 648, + 488, + 660 + ], + "score": 1.0, + "content": "need for more extensive testing of generative ML systems before their widespread deployment.", + "type": "text" + } + ], + "index": 47 + } + ], + "index": 43.5, + "bbox_fs": [ + 105, + 571, + 506, + 660 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 675, + 207, + 688 + ], + "lines": [ + { + "bbox": [ + 106, + 673, + 208, + 691 + ], + "spans": [ + { + "bbox": [ + 106, + 673, + 208, + 691 + ], + "score": 1.0, + "content": "Acknowledgements", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 48 + }, + { + "type": "text", + "bbox": [ + 107, + 699, + 505, + 722 + ], + "lines": [ + { + "bbox": [ + 105, + 698, + 505, + 713 + ], + "spans": [ + { + "bbox": [ + 105, + 698, + 505, + 713 + ], + "score": 1.0, + "content": "We thank the anonymous reviewers, Ruiqi Zhong, Jean-Stanislas Denain, Aditi Raghunathan, Jessy", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 105, + 710, + 506, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 506, + 723 + ], + "score": 1.0, + "content": "Lin, and Lawrence Chan for feedback. This work was supported by NSF Award Grant no. 1804794.", + "type": "text" + } + ], + "index": 50 + } + ], + "index": 49.5, + "bbox_fs": [ + 105, + 698, + 506, + 723 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "title", + "bbox": [ + 107, + 71, + 163, + 84 + ], + "lines": [ + { + "bbox": [ + 106, + 70, + 165, + 86 + ], + "spans": [ + { + "bbox": [ + 106, + 70, + 165, + 86 + ], + "score": 1.0, + "content": "References", + "type": "text" + } + ], + "index": 0 + } + ], + "index": 0 + }, + { + "type": "text", + "bbox": [ + 107, + 90, + 505, + 123 + ], + "lines": [ + { + "bbox": [ + 106, + 89, + 506, + 103 + ], + "spans": [ + { + "bbox": [ + 106, + 89, + 506, + 103 + ], + "score": 1.0, + "content": "Emily Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchel. On the dan-", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 115, + 101, + 507, + 115 + ], + "spans": [ + { + "bbox": [ + 115, + 101, + 507, + 115 + ], + "score": 1.0, + "content": "gers of stochastic parrots: Can language models be too big? In ACM Conference on Fairness,", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 115, + 112, + 315, + 124 + ], + "spans": [ + { + "bbox": [ + 115, + 112, + 315, + 124 + ], + "score": 1.0, + "content": "Accountability, and Transparency (FAccT), 2021.", + "type": "text" + } + ], + "index": 3 + } + ], + "index": 2 + }, + { + "type": "text", + "bbox": [ + 107, + 130, + 504, + 164 + ], + "lines": [ + { + "bbox": [ + 105, + 128, + 506, + 144 + ], + "spans": [ + { + "bbox": [ + 105, + 128, + 506, + 144 + ], + "score": 1.0, + "content": "Su Lin Blodgett, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, and Hanna Wallach. 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