| --- |
| license: cc-by-4.0 |
| 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 |
|
|
| <p align="center"> |
| <a href="https://github.com/Tencent/ElephantBench"> |
| <img |
| src="https://img.shields.io/badge/ElephantBench-GitHub-blue?logo=github" |
| alt="GitHub Repo" |
| /> |
| </a> |
| <a href="https://tencent.github.io/ElephantBench/"> |
| <img |
| src="https://img.shields.io/badge/ElephantBench-Leaderboard-ff725e?logo=githubpages&logoColor=white" |
| alt="ElephantBench Leaderboard" |
| /> |
| </a> |
| <a href="https://arxiv.org/abs/2608.28478"> |
| <img |
| src="https://img.shields.io/badge/ElephantBench-Paper-red?logo=arxiv&logoColor=red" |
| alt="Paper" |
| /> |
| </a> |
| </p> |
| |
| 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). |
|
|
|  |
|
|
| ## 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 |
|
|
| [cc-by-4.0](https://huggingface.co/datasets/tencent/ElephantBench/blob/main/LICENSE). |
|
|
| ## Citation |
|
|
| ``` |
| @article{pan2026elephantbench, |
| title={Blind Men and the Elephant: Probing the Epistemic Myopia of LLMs under Long-Tail Divergent Knowledge}, |
| author={Pan, Zhuoshi and Lu, Junru and Qian, Yan and Zhao, H. Vicky and Yin, Di and Sun, Xing}, |
| journal={arXiv preprint arXiv:2608.28478}, |
| year={2026} |
| } |
| ``` |
