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Observed inference time (s)": "{\"description\": \"min=4.722, mean=4.722, max=4.722, sum=4.722 (1)\", \"tab\": \"Efficiency\", \"score\": \"4.721950803041458\"}", + "NaturalQuestions (closed-book) - Observed inference time (s)": "{\"description\": \"min=0.659, mean=0.659, max=0.659, sum=0.659 (1)\", \"tab\": \"Efficiency\", \"score\": \"0.6590276186466217\"}", + "NaturalQuestions (open-book) - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}", + "NaturalQuestions (open-book) - # train": "{\"description\": \"min=5, mean=5, max=5, sum=5 (1)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "NaturalQuestions (open-book) - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "NaturalQuestions (open-book) - # prompt tokens": "{\"description\": \"min=1726.799, mean=1726.799, max=1726.799, sum=1726.799 (1)\", \"tab\": \"General information\", \"score\": \"1726.799\"}", + "NaturalQuestions (open-book) - # output tokens": "{\"description\": \"min=14.702, mean=14.702, max=14.702, sum=14.702 (1)\", \"tab\": \"General information\", \"score\": \"14.702\"}", + "NaturalQuestions (closed-book) - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}", + "NaturalQuestions (closed-book) - # train": "{\"description\": \"min=5, mean=5, max=5, sum=5 (1)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "NaturalQuestions (closed-book) - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "NaturalQuestions (closed-book) - # prompt tokens": "{\"description\": \"min=134.259, mean=134.259, max=134.259, sum=134.259 (1)\", \"tab\": \"General information\", \"score\": \"134.259\"}", + "NaturalQuestions (closed-book) - # output tokens": "{\"description\": \"min=8.63, mean=8.63, max=8.63, sum=8.63 (1)\", \"tab\": \"General information\", \"score\": \"8.63\"}" + } + }, + "generation_config": { + "additional_details": { + "mode": "\"closedbook\"" + } + } + }, + { + "evaluation_name": "OpenbookQA", + "source_data": { + "dataset_name": "OpenbookQA", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on OpenbookQA", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.966, + "details": { + "description": "min=0.966, mean=0.966, max=0.966, sum=0.966 (1)", + "tab": "Accuracy", + "OpenbookQA - Observed inference time (s)": "{\"description\": \"min=1.256, mean=1.256, max=1.256, sum=1.256 (1)\", \"tab\": \"Efficiency\", \"score\": \"1.2558565106391906\"}", + "OpenbookQA - # eval": "{\"description\": \"min=500, mean=500, max=500, sum=500 (1)\", \"tab\": \"General information\", \"score\": \"500.0\"}", + "OpenbookQA - # train": "{\"description\": \"min=5, mean=5, max=5, sum=5 (1)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "OpenbookQA - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "OpenbookQA - # prompt tokens": "{\"description\": \"min=263.79, mean=263.79, max=263.79, sum=263.79 (1)\", \"tab\": \"General information\", \"score\": \"263.79\"}", + "OpenbookQA - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=1 (1)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "dataset": "\"openbookqa\"", + "method": "\"multiple_choice_joint\"" + } + } + }, + { + "evaluation_name": "MMLU", + "source_data": { + "dataset_name": "MMLU", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on MMLU", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.809, + "details": { + "description": "min=0.63, mean=0.809, max=0.96, sum=4.047 (5)", + "tab": "Accuracy", + "MMLU - Observed inference time (s)": "{\"description\": \"min=0.66, mean=0.673, max=0.689, sum=3.367 (5)\", \"tab\": \"Efficiency\", \"score\": \"0.6733581468766195\"}", + "MMLU - # eval": "{\"description\": \"min=100, mean=102.8, max=114, sum=514 (5)\", \"tab\": \"General information\", \"score\": \"102.8\"}", + "MMLU - # train": "{\"description\": \"min=5, mean=5, max=5, sum=25 (5)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "MMLU - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (5)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "MMLU - # prompt tokens": "{\"description\": \"min=370.26, mean=478.747, max=619.596, sum=2393.736 (5)\", \"tab\": \"General information\", \"score\": \"478.747298245614\"}", + "MMLU - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=5 (5)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "[\"abstract_algebra\", \"college_chemistry\", \"computer_security\", \"econometrics\", \"us_foreign_policy\"]", + "method": "\"multiple_choice_joint\"" + } + } + }, + { + "evaluation_name": "MATH", + "source_data": { + "dataset_name": "MATH", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "Equivalent (CoT) on MATH", + "metric_name": "Equivalent (CoT)", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.904, + "details": { + "description": "min=0.789, mean=0.904, max=0.985, sum=6.326 (7)", + "tab": "Accuracy", + "MATH - Observed inference time (s)": "{\"description\": \"min=3.355, mean=4.052, max=4.718, sum=28.364 (7)\", \"tab\": \"Efficiency\", \"score\": \"4.0520609326088035\"}", + "MATH - # eval": "{\"description\": \"min=30, mean=62.429, max=135, sum=437 (7)\", \"tab\": \"General information\", \"score\": \"62.42857142857143\"}", + "MATH - # train": "{\"description\": \"min=8, mean=8, max=8, sum=56 (7)\", \"tab\": \"General information\", \"score\": \"8.0\"}", + "MATH - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (7)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "MATH - # prompt tokens": "{\"description\": \"min=887.259, mean=1301.814, max=2319.808, sum=9112.699 (7)\", \"tab\": \"General information\", \"score\": \"1301.8141219676104\"}", + "MATH - # output tokens": "{\"description\": \"min=127.663, mean=168.831, max=213.077, sum=1181.819 (7)\", \"tab\": \"General information\", \"score\": \"168.831271579864\"}" + } + }, + "generation_config": { + "additional_details": { + "subject": "[\"algebra\", \"counting_and_probability\", \"geometry\", \"intermediate_algebra\", \"number_theory\", \"prealgebra\", \"precalculus\"]", + "level": "\"1\"", + "use_official_examples": "\"False\"", + "use_chain_of_thought": "\"True\"" + } + } + }, + { + "evaluation_name": "GSM8K", + "source_data": { + "dataset_name": "GSM8K", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on GSM8K", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.956, + "details": { + "description": "min=0.956, mean=0.956, max=0.956, sum=0.956 (1)", + "tab": "Accuracy", + "GSM8K - Observed inference time (s)": "{\"description\": \"min=3.518, mean=3.518, max=3.518, sum=3.518 (1)\", \"tab\": \"Efficiency\", \"score\": \"3.5175547733306884\"}", + "GSM8K - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}", + "GSM8K - # train": "{\"description\": \"min=5, mean=5, max=5, sum=5 (1)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "GSM8K - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "GSM8K - # prompt tokens": "{\"description\": \"min=938.712, mean=938.712, max=938.712, sum=938.712 (1)\", \"tab\": \"General information\", \"score\": \"938.712\"}", + "GSM8K - # output tokens": "{\"description\": \"min=141.152, mean=141.152, max=141.152, sum=141.152 (1)\", \"tab\": \"General information\", \"score\": \"141.152\"}" + } + }, + "generation_config": { + "additional_details": {} + } + }, + { + "evaluation_name": "LegalBench", + "source_data": { + "dataset_name": "LegalBench", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on LegalBench", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.647, + "details": { + "description": "min=0.283, mean=0.647, max=0.989, sum=3.237 (5)", + "tab": "Accuracy", + "LegalBench - Observed inference time (s)": "{\"description\": \"min=0.559, mean=1.013, max=1.649, sum=5.065 (5)\", \"tab\": \"Efficiency\", \"score\": \"1.0130474324650445\"}", + "LegalBench - # eval": "{\"description\": \"min=95, mean=409.4, max=1000, sum=2047 (5)\", \"tab\": \"General information\", \"score\": \"409.4\"}", + "LegalBench - # train": "{\"description\": \"min=4, mean=4.8, max=5, sum=24 (5)\", \"tab\": \"General information\", \"score\": \"4.8\"}", + "LegalBench - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (5)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "LegalBench - # prompt tokens": "{\"description\": \"min=232.653, mean=1568.242, max=6432.398, sum=7841.208 (5)\", \"tab\": \"General information\", \"score\": \"1568.241581367783\"}", + "LegalBench - # output tokens": "{\"description\": \"min=1, mean=3.7, max=13.488, sum=18.498 (5)\", \"tab\": \"General information\", \"score\": \"3.6996529470816006\"}" + } + }, + "generation_config": { + "additional_details": { + "subset": "[\"abercrombie\", \"corporate_lobbying\", \"function_of_decision_section\", \"international_citizenship_questions\", \"proa\"]" + } + } + }, + { + "evaluation_name": "MedQA", + "source_data": { + "dataset_name": "MedQA", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on MedQA", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.859, + "details": { + "description": "min=0.859, mean=0.859, max=0.859, sum=0.859 (1)", + "tab": "Accuracy", + "MedQA - Observed inference time (s)": "{\"description\": \"min=0.815, mean=0.815, max=0.815, sum=0.815 (1)\", \"tab\": \"Efficiency\", \"score\": \"0.8153728936348947\"}", + "MedQA - # eval": "{\"description\": \"min=503, mean=503, max=503, sum=503 (1)\", \"tab\": \"General information\", \"score\": \"503.0\"}", + "MedQA - # train": "{\"description\": \"min=5, mean=5, max=5, sum=5 (1)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "MedQA - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "MedQA - # prompt tokens": "{\"description\": \"min=1027.437, mean=1027.437, max=1027.437, sum=1027.437 (1)\", \"tab\": \"General information\", \"score\": \"1027.4373757455269\"}", + "MedQA - # output tokens": "{\"description\": \"min=1, mean=1, max=1, sum=1 (1)\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": {} + } + }, + { + "evaluation_name": "WMT 2014", + "source_data": { + "dataset_name": "WMT 2014", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "BLEU-4 on WMT 2014", + "metric_name": "BLEU-4", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.226, + "details": { + "description": "min=0.174, mean=0.226, max=0.266, sum=1.128 (5)", + "tab": "Accuracy", + "WMT 2014 - Observed inference time (s)": "{\"description\": \"min=0.838, mean=0.86, max=0.889, sum=4.301 (5)\", \"tab\": \"Efficiency\", \"score\": \"0.8602394085223064\"}", + "WMT 2014 - # eval": "{\"description\": \"min=503, mean=568.8, max=832, sum=2844 (5)\", \"tab\": \"General information\", \"score\": \"568.8\"}", + "WMT 2014 - # train": "{\"description\": \"min=1, mean=1, max=1, sum=5 (5)\", \"tab\": \"General information\", \"score\": \"1.0\"}", + "WMT 2014 - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (5)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "WMT 2014 - # prompt tokens": "{\"description\": \"min=141.406, mean=162.573, max=184.974, sum=812.866 (5)\", \"tab\": \"General information\", \"score\": \"162.5732207715247\"}", + "WMT 2014 - # output tokens": "{\"description\": \"min=23.825, mean=25.177, max=25.958, sum=125.887 (5)\", \"tab\": \"General information\", \"score\": \"25.177411492582966\"}" + } + }, + "generation_config": { + "additional_details": { + "language_pair": "[\"cs-en\", \"de-en\", \"fr-en\", \"hi-en\", \"ru-en\"]" + } + } + } + ] +} \ No newline at end of file diff --git a/flat/objects/0a/76/0a765c0c-f8f5-47be-9fb3-d2519730cd97.json b/flat/objects/0a/76/0a765c0c-f8f5-47be-9fb3-d2519730cd97.json new file mode 100644 index 0000000000000000000000000000000000000000..f9d187ec57ca6539928071821405e1748ff1f69f --- /dev/null +++ b/flat/objects/0a/76/0a765c0c-f8f5-47be-9fb3-d2519730cd97.json @@ -0,0 +1,268 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "MathChat/DeepSeek-Math/1771591481.616601", + "retrieved_timestamp": "1771591481.616601", + "source_metadata": { + "source_name": "alphaXiv State of the Art", + "source_type": "documentation", + "source_organization_name": "alphaXiv", + "source_organization_url": "https://alphaxiv.org", + "evaluator_relationship": "third_party", + "additional_details": { + "alphaxiv_dataset_org": "University of Notre Dame", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "DeepSeek-Math", + "name": "DeepSeek-Math", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "MathChat", + "source_data": { + "dataset_name": "MathChat", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2405.19444" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "The Overall Average score on the MathChat benchmark, which is the average of ten normalized sub-metrics across all four tasks (Follow-up QA, Error Correction, Error Analysis, Problem Generation). This metric provides the most comprehensive single-figure summary of a model's performance. Scores are normalized to a 0-1 scale. Results are for 7B parameter models.", + "additional_details": { + "alphaxiv_y_axis": "Overall Average Score", + "alphaxiv_is_primary": "True", + "raw_evaluation_name": "MathChat Benchmark: Overall Average Score (7B Models)" + }, + "metric_id": "mathchat_benchmark_overall_average_score_7b_models", + "metric_name": "MathChat Benchmark: Overall Average Score (7B Models)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.452 + }, + "evaluation_result_id": "MathChat/DeepSeek-Math/1771591481.616601#mathchat#mathchat_benchmark_overall_average_score_7b_models" + }, + { + "evaluation_name": "MathChat", + "source_data": { + "dataset_name": "MathChat", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2405.19444" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Instruction Following (IF) score on the Error Analysis task, evaluated by GPT-4 on a scale of 1 to 5. This open-ended task requires the model to recognize, analyze, and correct an error in a given solution, testing its diagnostic reasoning.", + "additional_details": { + "alphaxiv_y_axis": "Score (1-5)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "MathChat: Error Analysis - Instruction Following Score" + }, + "metric_id": "mathchat_error_analysis_instruction_following_score", + "metric_name": "MathChat: Error Analysis - Instruction Following Score", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 1.87 + }, + "evaluation_result_id": "MathChat/DeepSeek-Math/1771591481.616601#mathchat#mathchat_error_analysis_instruction_following_score" + }, + { + "evaluation_name": "MathChat", + "source_data": { + "dataset_name": "MathChat", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2405.19444" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Accuracy on the Error Correction task in the MathChat benchmark. 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Models marked with '*' were evaluated by the C-Eval team.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "True", + "raw_evaluation_name": "C-Eval Leaderboard: Overall Average Accuracy" + }, + "metric_id": "c_eval_leaderboard_overall_average_accuracy", + "metric_name": "C-Eval Leaderboard: Overall Average Accuracy", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 90.1 + }, + "evaluation_result_id": "C-Eval/MagicLM/1771591481.616601#c_eval#c_eval_leaderboard_overall_average_accuracy" + }, + { + "evaluation_name": "C-Eval", + "source_data": { + "dataset_name": "C-Eval", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2305.08322" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Average accuracy on the C-EVAL HARD subset, which is composed of particularly challenging subjects requiring advanced reasoning (e.g., advanced mathematics, college physics). This metric from the official leaderboard is designed to differentiate the reasoning capabilities of the most powerful models. The leaderboard includes results from models with both open and limited access. Models marked with '*' were evaluated by the C-Eval team.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "C-Eval Leaderboard: Average Accuracy (Hard Subjects)" + }, + "metric_id": "c_eval_leaderboard_average_accuracy_hard_subjects", + "metric_name": "C-Eval Leaderboard: Average Accuracy (Hard Subjects)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 80.7 + }, + "evaluation_result_id": "C-Eval/MagicLM/1771591481.616601#c_eval#c_eval_leaderboard_average_accuracy_hard_subjects" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/0a/98/0a98d81a-c503-4bef-9964-d21f95bfbdf9.json b/flat/objects/0a/98/0a98d81a-c503-4bef-9964-d21f95bfbdf9.json new file mode 100644 index 0000000000000000000000000000000000000000..47a58fd7196c782834279e5e91e5e47fa6435f1b --- /dev/null +++ b/flat/objects/0a/98/0a98d81a-c503-4bef-9964-d21f95bfbdf9.json @@ -0,0 +1,328 @@ +{ + "schema_version": "0.2.2", + "evaluation_id": "Multilingual Compositional Wikidata Questions/mT5-small+RIR/1771591481.616601", + "retrieved_timestamp": "1771591481.616601", + "source_metadata": { + "source_name": "alphaXiv State of the Art", + "source_type": "documentation", + "source_organization_name": "alphaXiv", + "source_organization_url": "https://alphaxiv.org", + "evaluator_relationship": "third_party", + "additional_details": { + "alphaxiv_dataset_org": "University of Copenhagen", + "alphaxiv_dataset_type": "text", + "scrape_source": "https://github.com/alphaXiv/feedback/issues/189" + } + }, + "model_info": { + "id": "mT5-small+RIR", + "name": "mT5-small+RIR", + "developer": "unknown" + }, + "evaluation_results": [ + { + "evaluation_name": "Multilingual Compositional Wikidata Questions", + "source_data": { + "dataset_name": "Multilingual Compositional Wikidata Questions", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2108.03509" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates zero-shot cross-lingual transfer from English to Chinese on the MCWQ benchmark. Models are trained only on English data and evaluated on Chinese. The score is the mean Exact Match accuracy across the three Maximum Compound Divergence (MCD) splits. 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See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Robustness\", \"score\": \"0.33838638278361\"}", + "NaturalQuestions (closed-book) - F1 (Fairness)": "{\"description\": \"min=0.139, mean=0.147, max=0.151, sum=0.44 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Fairness\", \"score\": \"0.14670404179376148\"}", + "NaturalQuestions (open-book) - F1 (Fairness)": "{\"description\": \"min=0.446, mean=0.479, max=0.506, sum=1.436 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Fairness\", \"score\": \"0.47851717891712475\"}", + "NaturalQuestions (closed-book) - Denoised inference time (s)": "{\"description\": \"min=0.116, mean=0.122, max=0.128, sum=0.367 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Efficiency\", \"score\": \"0.12234622395833335\"}", + "NaturalQuestions (open-book) - Denoised inference time (s)": "{\"description\": \"min=0.166, mean=0.189, max=0.21, sum=0.566 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Efficiency\", \"score\": \"0.18882224978298598\"}", + "NaturalQuestions (closed-book) - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=3000 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"1000.0\"}", + "NaturalQuestions (closed-book) - # train": "{\"description\": \"min=5, mean=5, max=5, sum=15 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "NaturalQuestions (closed-book) - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "NaturalQuestions (closed-book) - # prompt tokens": "{\"description\": \"min=110.254, mean=112.254, max=116.254, sum=336.762 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"112.254\"}", + "NaturalQuestions (closed-book) - # output tokens": "{\"description\": \"min=5.376, mean=6.313, max=7.104, sum=18.94 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"6.3133333333333335\"}", + "NaturalQuestions (closed-book) - # trials": "{\"description\": \"min=3, mean=3, max=3, sum=9 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"3.0\"}", + "NaturalQuestions (open-book) - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=3000 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"1000.0\"}", + "NaturalQuestions (open-book) - # train": "{\"description\": \"min=4.647, mean=4.691, max=4.724, sum=14.074 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"4.691333333333334\"}", + "NaturalQuestions (open-book) - truncated": "{\"description\": \"min=0.036, mean=0.036, max=0.036, sum=0.108 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"0.036\"}", + "NaturalQuestions (open-book) - # prompt tokens": "{\"description\": \"min=1231.212, mean=1419.574, max=1523.257, sum=4258.721 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"1419.5736666666664\"}", + "NaturalQuestions (open-book) - # output tokens": "{\"description\": \"min=9.89, mean=12.581, max=15.337, sum=37.742 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"12.580666666666668\"}", + "NaturalQuestions (open-book) - # trials": "{\"description\": \"min=3, mean=3, max=3, sum=9 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"3.0\"}", + "NaturalQuestions (closed-book) - Stereotypes (race)": "{\"description\": \"(0)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Bias\", \"score\": \"\"}", + "NaturalQuestions (closed-book) - Stereotypes (gender)": "{\"description\": \"min=0.5, mean=0.5, max=0.5, sum=1 (2)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Bias\", \"score\": \"0.5\"}", + "NaturalQuestions (closed-book) - Representation (race)": "{\"description\": \"min=0.291, mean=0.415, max=0.509, sum=1.245 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Bias\", \"score\": \"0.4150858887700994\"}", + "NaturalQuestions (closed-book) - Representation (gender)": "{\"description\": \"min=0.119, mean=0.203, max=0.25, sum=0.608 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Bias\", \"score\": \"0.20272601794340928\"}", + "NaturalQuestions (open-book) - Stereotypes (race)": "{\"description\": \"min=0.667, mean=0.667, max=0.667, sum=0.667 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Bias\", \"score\": \"0.6666666666666667\"}", + "NaturalQuestions (open-book) - Stereotypes (gender)": "{\"description\": \"min=0.407, mean=0.469, max=0.5, sum=1.407 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Bias\", \"score\": \"0.469047619047619\"}", + "NaturalQuestions (open-book) - Representation (race)": "{\"description\": \"min=0.441, mean=0.453, max=0.467, sum=1.359 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Bias\", \"score\": \"0.4528357579590976\"}", + "NaturalQuestions (open-book) - Representation (gender)": "{\"description\": \"min=0.361, mean=0.379, max=0.397, sum=1.136 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Bias\", \"score\": \"0.3786428074398272\"}", + "NaturalQuestions (closed-book) - Toxic fraction": "{\"description\": \"min=0, mean=0, max=0, sum=0 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Toxicity\", \"score\": \"0.0\"}", + "NaturalQuestions (open-book) - Toxic fraction": "{\"description\": \"min=0.001, mean=0.002, max=0.003, sum=0.005 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Toxicity\", \"score\": \"0.0016666666666666668\"}" + } + }, + "generation_config": { + "additional_details": {} + } + }, + { + "evaluation_name": "QuAC", + "source_data": { + "dataset_name": "QuAC", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/classic/benchmark_output/releases/v0.4.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "F1 on QuAC", + "metric_name": "F1", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.321, + "details": { + "description": "min=0.312, mean=0.321, max=0.335, sum=0.963 (3)\n⚠ Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.", + "tab": "Accuracy", + "QuAC - ECE (10-bin)": "{\"description\": \"min=0.033, mean=0.043, max=0.055, sum=0.129 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Calibration\", \"score\": \"0.04303687950629059\"}", + "QuAC - F1 (Robustness)": "{\"description\": \"min=0.164, mean=0.171, max=0.178, sum=0.513 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Robustness\", \"score\": \"0.1711623480279509\"}", + "QuAC - F1 (Fairness)": "{\"description\": \"min=0.241, mean=0.243, max=0.245, sum=0.728 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Fairness\", \"score\": \"0.24255939370982219\"}", + "QuAC - Denoised inference time (s)": "{\"description\": \"min=0.31, mean=0.323, max=0.34, sum=0.968 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Efficiency\", \"score\": \"0.32252038281250045\"}", + "QuAC - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=3000 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"1000.0\"}", + "QuAC - # train": "{\"description\": \"min=0.845, mean=0.944, max=1.086, sum=2.833 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"0.9443333333333334\"}", + "QuAC - truncated": "{\"description\": \"min=0.016, mean=0.016, max=0.016, sum=0.048 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"0.016\"}", + "QuAC - # prompt tokens": "{\"description\": \"min=1625.523, mean=1644.831, max=1670.605, sum=4934.492 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"1644.8306666666667\"}", + "QuAC - # output tokens": "{\"description\": \"min=29.104, mean=31.034, max=33.548, sum=93.102 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"31.034000000000002\"}", + "QuAC - # trials": "{\"description\": \"min=3, mean=3, max=3, sum=9 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"3.0\"}", + "QuAC - Stereotypes (race)": "{\"description\": \"min=0.633, mean=0.645, max=0.667, sum=1.936 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Bias\", \"score\": \"0.6454545454545455\"}", + "QuAC - Stereotypes (gender)": "{\"description\": \"min=0.426, mean=0.439, max=0.452, sum=1.317 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Bias\", \"score\": \"0.4390862600512319\"}", + "QuAC - Representation (race)": "{\"description\": \"min=0.2, mean=0.246, max=0.271, sum=0.738 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Bias\", \"score\": \"0.24599483204134365\"}", + "QuAC - Representation (gender)": "{\"description\": \"min=0.226, mean=0.231, max=0.234, sum=0.693 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Bias\", \"score\": \"0.23109052551695608\"}", + "QuAC - Toxic fraction": "{\"description\": \"min=0.002, mean=0.003, max=0.003, sum=0.008 (3)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Toxicity\", \"score\": \"0.0026666666666666666\"}" + } + }, + "generation_config": { + "additional_details": {} + } + }, + { + "evaluation_name": "HellaSwag", + "source_data": { + "dataset_name": "HellaSwag", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/classic/benchmark_output/releases/v0.4.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on HellaSwag", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.682, + "details": { + "description": "min=0.682, mean=0.682, max=0.682, sum=0.682 (1)\n⚠ Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.", + "tab": "Accuracy", + "HellaSwag - ECE (10-bin)": "{\"description\": \"min=0.25, mean=0.25, max=0.25, sum=0.25 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Calibration\", \"score\": \"0.24965148877506194\"}", + "HellaSwag - EM (Robustness)": "{\"description\": \"min=0.632, mean=0.632, max=0.632, sum=0.632 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Robustness\", \"score\": \"0.632\"}", + "HellaSwag - EM (Fairness)": "{\"description\": \"min=0.522, mean=0.522, max=0.522, sum=0.522 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Fairness\", \"score\": \"0.522\"}", + "HellaSwag - Denoised inference time (s)": "{\"description\": \"min=0.084, mean=0.084, max=0.084, sum=0.084 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Efficiency\", \"score\": \"0.08380637499999992\"}", + "HellaSwag - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"1000.0\"}", + "HellaSwag - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "HellaSwag - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "HellaSwag - # prompt tokens": "{\"description\": \"min=87.888, mean=87.888, max=87.888, sum=87.888 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"87.888\"}", + "HellaSwag - # output tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "HellaSwag - # trials": "{\"description\": \"min=1, mean=1, max=1, sum=1 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": {} + } + }, + { + "evaluation_name": "OpenbookQA", + "source_data": { + "dataset_name": "OpenbookQA", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/classic/benchmark_output/releases/v0.4.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on OpenbookQA", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.502, + "details": { + "description": "min=0.502, mean=0.502, max=0.502, sum=0.502 (1)\n⚠ Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.", + "tab": "Accuracy", + "OpenbookQA - ECE (10-bin)": "{\"description\": \"min=0.26, mean=0.26, max=0.26, sum=0.26 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Calibration\", \"score\": \"0.25956257561884827\"}", + "OpenbookQA - EM (Robustness)": "{\"description\": \"min=0.396, mean=0.396, max=0.396, sum=0.396 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Robustness\", \"score\": \"0.396\"}", + "OpenbookQA - EM (Fairness)": "{\"description\": \"min=0.43, mean=0.43, max=0.43, sum=0.43 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. 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See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"Efficiency\", \"score\": \"0.07928820312499986\"}", + "OpenbookQA - # eval": "{\"description\": \"min=500, mean=500, max=500, sum=500 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"500.0\"}", + "OpenbookQA - # train": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "OpenbookQA - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "OpenbookQA - # prompt tokens": "{\"description\": \"min=5.27, mean=5.27, max=5.27, sum=5.27 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"5.27\"}", + "OpenbookQA - # output tokens": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "OpenbookQA - # trials": "{\"description\": \"min=1, mean=1, max=1, sum=1 (1)\\n\\u26a0 Brown et al. perform an analysis of the contamination for GPT-3 and its known derivatives. For these datasets, they find that 1% - 6% of the datasets' test instances are contaminated based on N-gram overlap, and model performance does not substantially change for these datasets. See Table C.1 on page 45 of https://arxiv.org/pdf/2005.14165.pdf.\", \"tab\": \"General information\", \"score\": \"1.0\"}" + } + }, + "generation_config": { + "additional_details": {} + } + }, + { + "evaluation_name": "TruthfulQA", + "source_data": { + "dataset_name": "TruthfulQA", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/classic/benchmark_output/releases/v0.4.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on TruthfulQA", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.232, + "details": { + "description": "min=0.222, mean=0.232, max=0.251, sum=0.696 (3)", + "tab": "Accuracy", + "TruthfulQA - ECE (10-bin)": "{\"description\": \"min=0.05, mean=0.062, max=0.072, sum=0.186 (3)\", \"tab\": \"Calibration\", \"score\": \"0.06204978796421436\"}", + "TruthfulQA - EM (Robustness)": "{\"description\": \"min=0.167, mean=0.186, max=0.214, sum=0.557 (3)\", \"tab\": \"Robustness\", \"score\": \"0.1855249745158002\"}", + "TruthfulQA - 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Observed inference time (s)": "{\"description\": \"min=2.543, mean=2.543, max=2.543, sum=2.543 (1)\", \"tab\": \"Efficiency\", \"score\": \"2.543274956703186\"}", + "GSM8K - # eval": "{\"description\": \"min=1000, mean=1000, max=1000, sum=1000 (1)\", \"tab\": \"General information\", \"score\": \"1000.0\"}", + "GSM8K - # train": "{\"description\": \"min=5, mean=5, max=5, sum=5 (1)\", \"tab\": \"General information\", \"score\": \"5.0\"}", + "GSM8K - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (1)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "GSM8K - # prompt tokens": "{\"description\": \"min=938.869, mean=938.869, max=938.869, sum=938.869 (1)\", \"tab\": \"General information\", \"score\": \"938.869\"}", + "GSM8K - # output tokens": "{\"description\": \"min=89.718, mean=89.718, max=89.718, sum=89.718 (1)\", \"tab\": \"General information\", \"score\": \"89.718\"}" + } + }, + "generation_config": { + "additional_details": {} + } + }, + { + "evaluation_name": "LegalBench", + "source_data": { + "dataset_name": "LegalBench", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on LegalBench", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.644, + "details": { + "description": "min=0.33, mean=0.644, max=0.989, sum=3.221 (5)", + "tab": "Accuracy", + "LegalBench - Observed inference time (s)": "{\"description\": \"min=0.425, mean=0.731, max=1.784, sum=3.657 (5)\", \"tab\": \"Efficiency\", \"score\": \"0.7313747247589137\"}", + "LegalBench - # eval": "{\"description\": \"min=95, mean=409.4, max=1000, sum=2047 (5)\", \"tab\": \"General information\", \"score\": \"409.4\"}", + "LegalBench - # train": "{\"description\": \"min=3.984, mean=4.597, max=5, sum=22.984 (5)\", \"tab\": \"General information\", \"score\": \"4.596734693877551\"}", + "LegalBench - truncated": "{\"description\": \"min=0, mean=0, max=0, sum=0 (5)\", \"tab\": \"General information\", \"score\": \"0.0\"}", + "LegalBench - # prompt tokens": "{\"description\": \"min=205.632, mean=1355.759, max=5467.178, sum=6778.793 (5)\", \"tab\": \"General information\", \"score\": \"1355.7586406214054\"}", + "LegalBench - # output tokens": "{\"description\": \"min=1, mean=2.077, max=5.406, sum=10.386 (5)\", \"tab\": \"General information\", \"score\": \"2.0771673311343752\"}" + } + }, + "generation_config": { + "additional_details": { + "subset": "[\"abercrombie\", \"corporate_lobbying\", \"function_of_decision_section\", \"international_citizenship_questions\", \"proa\"]" + } + } + }, + { + "evaluation_name": "MedQA", + "source_data": { + "dataset_name": "MedQA", + "source_type": "url", + "url": [ + "https://storage.googleapis.com/crfm-helm-public/lite/benchmark_output/releases/v1.13.0/groups/core_scenarios.json" + ] + }, + "metric_config": { + "evaluation_description": "EM on MedQA", + "metric_name": "EM", + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 1.0 + }, + "score_details": { + "score": 0.598, + "details": { + "description": "min=0.598, mean=0.598, max=0.598, sum=0.598 (1)", + "tab": "Accuracy", + "MedQA - 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Higher recall means the agent successfully contacted more of the necessary users.", + "additional_details": { + "alphaxiv_y_axis": "Info Source Recall - Reactive Agent (DocCreation)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "PEOPLEJOIN-DOCCREATION: Information Source Recall (Reactive Agent)" + }, + "metric_id": "peoplejoin_doccreation_information_source_recall_reactive_agent", + "metric_name": "PEOPLEJOIN-DOCCREATION: Information Source Recall (Reactive Agent)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 0.8 + }, + "evaluation_result_id": "PEOPLEJOIN/GPT-4o/1771591481.616601#peoplejoin#peoplejoin_doccreation_information_source_recall_reactive_agent" + }, + { + "evaluation_name": "PEOPLEJOIN", + "source_data": { + "dataset_name": "PEOPLEJOIN", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2502.12328" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the recall of the set of distinct users contacted by the 'Reactive' agent relative to the ground-truth optimal set of users required to answer the query in the PEOPLEJOIN-QA task. 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Higher scores are better.", + "additional_details": { + "alphaxiv_y_axis": "AVSSD Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Audio-Visual Sound Source Detection (AVSSD) on VGGSS" + }, + "metric_id": "audio_visual_sound_source_detection_avssd_on_vggss", + "metric_name": "Audio-Visual Sound Source Detection (AVSSD) on VGGSS", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 50.5 + }, + "evaluation_result_id": "AVEB/FAVOR 7B (audio-visual)/1771591481.616601#aveb#audio_visual_sound_source_detection_avssd_on_vggss" + }, + { + "evaluation_name": "AVEB", + "source_data": { + "dataset_name": "AVEB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2310.05863" + ] + }, + "metric_config": { + "lower_is_better": true, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Word Error Rate (WER) for Audio-Visual Speech Recognition on the How2 dev5 dataset. 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Higher scores are better.", + "additional_details": { + "alphaxiv_y_axis": "ISQA Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Image Spoken Question Answering (ISQA) Accuracy" + }, + "metric_id": "image_spoken_question_answering_isqa_accuracy", + "metric_name": "Image Spoken Question Answering (ISQA) Accuracy", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 24.5 + }, + "evaluation_result_id": "AVEB/FAVOR 7B (audio-visual)/1771591481.616601#aveb#image_spoken_question_answering_isqa_accuracy" + }, + { + "evaluation_name": "AVEB", + "source_data": { + "dataset_name": "AVEB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2310.05863" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Top-1 accuracy for Optical Character Recognition-based question answering on the TextVQA test set. 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The task is framed as an in-context multiple-choice problem and evaluates the model's ability to understand temporal and causal relationships in video clips. The FAVOR model shows a significant improvement of over 20% absolute accuracy compared to baselines.", + "additional_details": { + "alphaxiv_y_axis": "Accuracy (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "Video Question Answering (Video QA) on NExT-QA" + }, + "metric_id": "video_question_answering_video_qa_on_next_qa", + "metric_name": "Video Question Answering (Video QA) on NExT-QA", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 42.5 + }, + "evaluation_result_id": "AVEB/FAVOR 7B (audio-visual)/1771591481.616601#aveb#video_question_answering_video_qa_on_next_qa" + }, + { + "evaluation_name": "AVEB", + "source_data": { + "dataset_name": "AVEB", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2310.05863" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "SPIDEr (SPICE + CIDEr) score for audio captioning on the AudioCaps test set. 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In this mode, the model operates autonomously. This metric assesses the quality and correctness of the code generated by the model.", + "additional_details": { + "alphaxiv_y_axis": "Executable Rate - End-to-End (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CIBench Executable Rate (End-to-End)" + }, + "metric_id": "cibench_executable_rate_end_to_end", + "metric_name": "CIBench Executable Rate (End-to-End)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 91.1 + }, + "evaluation_result_id": "CIBench/Llama-3-70B-Instruct/1771591481.616601#cibench#cibench_executable_rate_end_to_end" + }, + { + "evaluation_name": "CIBench", + "source_data": { + "dataset_name": "CIBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.10499" + ] + }, + "metric_config": { + "lower_is_better": true, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Indicates the percentage of generated code that executes without errors in the 'oracle' mode. In this mode, the model receives correct code snippets upon failure, simulating human guidance. This measures the model's coding ability when given corrective help.", + "additional_details": { + "alphaxiv_y_axis": "Executable Rate - Oracle (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CIBench Executable Rate (Oracle)" + }, + "metric_id": "cibench_executable_rate_oracle", + "metric_name": "CIBench Executable Rate (Oracle)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 82.8 + }, + "evaluation_result_id": "CIBench/Llama-3-70B-Instruct/1771591481.616601#cibench#cibench_executable_rate_oracle" + }, + { + "evaluation_name": "CIBench", + "source_data": { + "dataset_name": "CIBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.10499" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the accuracy of numerical results produced by the model in the 'end-to-end' mode, where the model solves problems autonomously without any human guidance. This assesses the model's ability to generate correct numerical outputs for data science tasks.", + "additional_details": { + "alphaxiv_y_axis": "Numeric Accuracy - End-to-End (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CIBench Numeric Accuracy (End-to-End)" + }, + "metric_id": "cibench_numeric_accuracy_end_to_end", + "metric_name": "CIBench Numeric Accuracy (End-to-End)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 64.9 + }, + "evaluation_result_id": "CIBench/Llama-3-70B-Instruct/1771591481.616601#cibench#cibench_numeric_accuracy_end_to_end" + }, + { + "evaluation_name": "CIBench", + "source_data": { + "dataset_name": "CIBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.10499" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the accuracy of numerical results produced by the model in the 'oracle' mode. In this mode, the model receives correct code snippets upon failure, simulating human guidance. This assesses the model's potential performance on numerical tasks when assisted.", + "additional_details": { + "alphaxiv_y_axis": "Numeric Accuracy - Oracle (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CIBench Numeric Accuracy (Oracle)" + }, + "metric_id": "cibench_numeric_accuracy_oracle", + "metric_name": "CIBench Numeric Accuracy (Oracle)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 72.9 + }, + "evaluation_result_id": "CIBench/Llama-3-70B-Instruct/1771591481.616601#cibench#cibench_numeric_accuracy_oracle" + }, + { + "evaluation_name": "CIBench", + "source_data": { + "dataset_name": "CIBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.10499" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the quality of structured text output using the ROUGE metric in the 'end-to-end' mode. In this mode, the model solves problems autonomously without any human guidance. This assesses the model's ability to generate accurate and well-formatted textual results.", + "additional_details": { + "alphaxiv_y_axis": "Text Score - End-to-End (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CIBench Text Score (End-to-End)" + }, + "metric_id": "cibench_text_score_end_to_end", + "metric_name": "CIBench Text Score (End-to-End)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 55.7 + }, + "evaluation_result_id": "CIBench/Llama-3-70B-Instruct/1771591481.616601#cibench#cibench_text_score_end_to_end" + }, + { + "evaluation_name": "CIBench", + "source_data": { + "dataset_name": "CIBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.10499" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the quality of structured text output using the ROUGE metric in the 'oracle' mode. In this mode, the model receives correct code snippets upon failure, simulating human guidance. This assesses the model's potential performance on text generation tasks when assisted.", + "additional_details": { + "alphaxiv_y_axis": "Text Score - Oracle (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CIBench Text Score (Oracle)" + }, + "metric_id": "cibench_text_score_oracle", + "metric_name": "CIBench Text Score (Oracle)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 74.2 + }, + "evaluation_result_id": "CIBench/Llama-3-70B-Instruct/1771591481.616601#cibench#cibench_text_score_oracle" + }, + { + "evaluation_name": "CIBench", + "source_data": { + "dataset_name": "CIBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.10499" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "A process-oriented metric that measures the proportion of instances where the model correctly invokes a code interpreter in the 'end-to-end' mode. In this mode, the model operates autonomously. This metric assesses the model's reliability in identifying when to use its coding tool.", + "additional_details": { + "alphaxiv_y_axis": "Tool Call Rate - End-to-End (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CIBench Tool Call Rate (End-to-End)" + }, + "metric_id": "cibench_tool_call_rate_end_to_end", + "metric_name": "CIBench Tool Call Rate (End-to-End)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 98 + }, + "evaluation_result_id": "CIBench/Llama-3-70B-Instruct/1771591481.616601#cibench#cibench_tool_call_rate_end_to_end" + }, + { + "evaluation_name": "CIBench", + "source_data": { + "dataset_name": "CIBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.10499" + ] + }, + "metric_config": { + "lower_is_better": true, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Measures the proportion of instances where the model correctly invokes a code interpreter in the 'oracle' mode. In this mode, the model receives correct code snippets upon failure, simulating human guidance. This metric assesses the model's ability to recognize the need for a tool when assisted.", + "additional_details": { + "alphaxiv_y_axis": "Tool Call Rate - Oracle (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CIBench Tool Call Rate (Oracle)" + }, + "metric_id": "cibench_tool_call_rate_oracle", + "metric_name": "CIBench Tool Call Rate (Oracle)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 96.6 + }, + "evaluation_result_id": "CIBench/Llama-3-70B-Instruct/1771591481.616601#cibench#cibench_tool_call_rate_oracle" + }, + { + "evaluation_name": "CIBench", + "source_data": { + "dataset_name": "CIBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.10499" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates the quality of visual output (e.g., plots) using structural similarities in the 'end-to-end' mode. In this mode, the model solves problems autonomously without any human guidance. This assesses the model's capability to generate correct data visualizations.", + "additional_details": { + "alphaxiv_y_axis": "Visualization Score - End-to-End (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CIBench Visualization Score (End-to-End)" + }, + "metric_id": "cibench_visualization_score_end_to_end", + "metric_name": "CIBench Visualization Score (End-to-End)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 63.6 + }, + "evaluation_result_id": "CIBench/Llama-3-70B-Instruct/1771591481.616601#cibench#cibench_visualization_score_end_to_end" + }, + { + "evaluation_name": "CIBench", + "source_data": { + "dataset_name": "CIBench", + "source_type": "url", + "url": [ + "https://www.alphaxiv.org/abs/2407.10499" + ] + }, + "metric_config": { + "lower_is_better": false, + "score_type": "continuous", + "min_score": 0.0, + "max_score": 100.0, + "evaluation_description": "Evaluates the quality of visual output (e.g., plots) using structural similarities in the 'oracle' mode. In this mode, the model receives correct code snippets upon failure, simulating human guidance. This assesses the model's potential performance on visualization tasks when assisted.", + "additional_details": { + "alphaxiv_y_axis": "Visualization Score - Oracle (%)", + "alphaxiv_is_primary": "False", + "raw_evaluation_name": "CIBench Visualization Score (Oracle)" + }, + "metric_id": "cibench_visualization_score_oracle", + "metric_name": "CIBench Visualization Score (Oracle)", + "metric_kind": "score", + "metric_unit": "points" + }, + "score_details": { + "score": 62 + }, + "evaluation_result_id": "CIBench/Llama-3-70B-Instruct/1771591481.616601#cibench#cibench_visualization_score_oracle" + } + ], + "eval_library": { + "name": "alphaxiv", + "version": "unknown" + } +} diff --git a/flat/objects/bf/73/bf737a56-ee66-42fd-81cd-ab3170cde94e.json b/flat/objects/bf/73/bf737a56-ee66-42fd-81cd-ab3170cde94e.json new file mode 100644 index 0000000000000000000000000000000000000000..2332ab3d71dab079a7e0dd43d8815c210384581e --- /dev/null +++ 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