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+Tencent is pleased to support the open source community by making ElephantBench available. 
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README.md

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+---
+license: apache-2.0
+language:
+- en
+task_categories:
+- question-answering
+pretty_name: ElephantBench
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/elephantbench.jsonl
+---
+
+# ElephantBench
+
+ElephantBench is a closed-book knowledge probe for evaluating whether a language model
+remembers long-tail facts and recalls the different verified accounts associated with them.
+The release contains 1,094 English questions.
+
+Evaluation code, prompts, construction utilities, and full documentation are available in
+the [ElephantBench GitHub repository](https://github.com/Tencent/ElephantBench).
+
+![ElephantBench overview](assets/benchmark_overview.png)
+
+## Load the dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Tencent/ElephantBench", split="test")
+```
+
+## Record format
+
+```json
+{
+  "benchmark_id": "4382fd6d-ec5d-5ec8-a3f2-8abc706fa010",
+  "item_group_id": "c9ef1c87-3e50-5190-8e8e-37a82ec25634",
+  "eval": {
+    "question": "What birth date was reported for Mother Teresa?",
+    "gold_answers": [
+      {"value": "August 26, 1910"},
+      {"value": "August 27, 1910"}
+    ],
+    "preferred_answer": "Reports cite August 26 and August 27, 1910."
+  }
+}
+```
+
+The target model receives only `eval.question`. Gold answers are supplied to the judge after
+generation.
+
+## Evaluation
+
+- **C (complete):** all verified answers are covered without a material contradiction.
+- **P (partial):** at least one, but not all, verified answers are covered.
+- **F (failed):** no verified answer is covered, a material contradiction is present, or the
+  generation/judging request failed.
+- **K (conditional completeness):** `C / (C + P)`.
+
+## License
+
+[Apache License 2.0](LICENSE).
+
+## Citation
+
+Citation details will be added with the accompanying paper.

data/README.md

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+# Dataset
+
+`elephantbench.jsonl` contains 1,094 records in JSON Lines format, with one question per line.
+The same release is mirrored at
+[Tencent/ElephantBench](https://github.com/Tencent/ElephantBench/blob/main/data/elephantbench.jsonl).
+
+Load the hosted test split with:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("tencent/ElephantBench", split="test")
+```
+
+## Example
+
+The text below is shortened for readability, but its keys and nesting match the dataset exactly.
+
+```json
+{
+  "benchmark_id": "4382fd6d-ec5d-5ec8-a3f2-8abc706fa010",
+  "item_group_id": "c9ef1c87-3e50-5190-8e8e-37a82ec25634",
+  "eval": {
+    "question": "What birth date is reported for Mother Teresa ...?",
+    "gold_answers": [
+      {"value": "August 26, 1910"},
+      {"value": "August 27, 1910"}
+    ],
+    "preferred_answer": "Sources report August 26 and August 27, 1910."
+  }
+}
+```
+
+## Record structure
+
+| Field | Description |
+| --- | --- |
+| `benchmark_id` | Canonical UUID identifying one question formulation. |
+| `item_group_id` | Canonical UUID grouping formulations derived from the same reviewed conflict. Every group contains at least two records, but group sizes are not otherwise fixed. |
+| `eval.question` | Question sent to the target model. |
+| `eval.gold_answers[].value` | Verified accounts used by the judge; complete recall requires covering all of them. |
+| `eval.preferred_answer` | Concise human-readable reference response. |
+
+The target model receives only `eval.question`. Gold answers are supplied to the judge after the response has been saved. Complete, partial, and failed recall are assigned mechanically from per-gold coverage and material contradictions.
+
+Both identifier fields use lowercase, hyphenated UUID strings. The construction exporter
+generates deterministic UUIDv5 values under the ElephantBench project namespace so repeated
+exports of the same source record retain stable identifiers. Consumers should otherwise treat
+the identifiers as opaque values.
+
+The release contains 1,094 unique normalized question texts. Every record has at least two verified answers. Source pages and review traces are not distributed with the benchmark and are never passed to evaluated models. The dataset is intended for research on factual recall and memory completeness; scores should not be interpreted as a general measure of model quality or source credibility.
+
+Verify file integrity from this directory with:
+
+```bash
+sha256sum -c SHA256SUMS
+```

data/SHA256SUMS

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+d8fbb47608e0cd14adb3cc16d1bcc356178469f5c86f7d89222d79667591119a  elephantbench.jsonl