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109 lines
5.3 KiB
Markdown
109 lines
5.3 KiB
Markdown
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# CHARM✨ Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations [ACL2024]
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[](https://arxiv.org/abs/2403.14112)
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[](./LICENSE)
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<div align="center">
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📃[Paper](https://arxiv.org/abs/2403.14112)
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🏰[Project Page](https://opendatalab.github.io/CHARM/)
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🏆[Leaderboard](https://opendatalab.github.io/CHARM/leaderboard.html)
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✨[Findings](https://opendatalab.github.io/CHARM/findings.html)
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</div>
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<div align="center">
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📖 <a href="./README_ZH.md"> 中文</a> | <a href="./README.md">English</a>
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</div>
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## 数据集介绍
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**CHARM** 是首个全面深入评估大型语言模型（LLMs）在中文常识推理能力的基准测试，它覆盖了国际普遍认知的常识以及独特的中国文化常识。此外，CHARM 还可以评估 LLMs 独立于记忆的推理能力，并分析其典型错误。
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## 与其他常识推理评测基准的比较
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<html lang="en">
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<table align="center">
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<thead class="fixed-header">
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<tr>
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<th>基准</th>
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<th>汉语</th>
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<th>常识推理</th>
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<th>中国特有知识</th>
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<th>中国和世界知识域</th>
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<th>推理和记忆的关系</th>
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</tr>
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</thead>
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<tr>
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<td><a href="https://arxiv.org/abs/2302.04752"> davis2023benchmarks</a> 中提到的基准</td>
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<td><strong><span style="color: red;">✘</span></strong></td>
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<td><strong><span style="color: green;">✔</span></strong></td>
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<td><strong><span style="color: red;">✘</span></strong></td>
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<td><strong><span style="color: red;">✘</span></strong></td>
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<td><strong><span style="color: red;">✘</span></strong></td>
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</tr>
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<tr>
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<td><a href="https://arxiv.org/abs/1809.05053"> XNLI</a>, <a
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href="https://arxiv.org/abs/2005.00333">XCOPA</a>,<a
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href="https://arxiv.org/abs/2112.10668">XStoryCloze</a></td>
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<td><strong><span style="color: green;">✔</span></strong></td>
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<td><strong><span style="color: green;">✔</span></strong></td>
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<td><strong><span style="color: red;">✘</span></strong></td>
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<td><strong><span style="color: red;">✘</span></strong></td>
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<td><strong><span style="color: red;">✘</span></strong></td>
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</tr>
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<tr>
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<td><a href="https://arxiv.org/abs/2007.08124">LogiQA</a>,<a
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href="https://arxiv.org/abs/2004.05986">CLUE</a>, <a
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href="https://arxiv.org/abs/2306.09212">CMMLU</a></td>
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<td><strong><span style="color: green;">✔</span></strong></td>
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<td><strong><span style="color: red;">✘</span></strong></td>
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<td><strong><span style="color: green;">✔</span></strong></td>
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<td><strong><span style="color: red;">✘</span></strong></td>
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<td><strong><span style="color: red;">✘</span></strong></td>
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</tr>
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<tr>
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<td><a href="https://arxiv.org/abs/2312.12853">CORECODE</a> </td>
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<td><strong><span style="color: green;">✔</span></strong></td>
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<td><strong><span style="color: green;">✔</span></strong></td>
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<td><strong><span style="color: red;">✘</span></strong></td>
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<td><strong><span style="color: red;">✘</span></strong></td>
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<td><strong><span style="color: red;">✘</span></strong></td>
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</tr>
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<tr>
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<td><strong><a href="https://arxiv.org/abs/2403.14112">CHARM (ours)</a> </strong></td>
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<td><strong><span style="color: green;">✔</span></strong></td>
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<td><strong><span style="color: green;">✔</span></strong></td>
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<td><strong><span style="color: green;">✔</span></strong></td>
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<td><strong><span style="color: green;">✔</span></strong></td>
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<td><strong><span style="color: green;">✔</span></strong></td>
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</tr>
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</table>
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## 🛠️ 如何使用
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以下是快速下载 CHARM 并在 OpenCompass 上进行评估的步骤。
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### 1. 下载 CHARM
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```bash
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git clone https://github.com/opendatalab/CHARM ${path_to_CHARM_repo}
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```
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### 2. 推理和评测
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```bash
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cd ${path_to_opencompass}
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mkdir -p data
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ln -snf ${path_to_CHARM_repo}/data/CHARM ./data/CHARM
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# 在CHARM上对模型hf_llama3_8b_instruct做推理和评测
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python run.py --models hf_llama3_8b_instruct --datasets charm_gen
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```
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## 🖊️ 引用
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```bibtex
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@misc{sun2024benchmarking,
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title={Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations},
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author={Jiaxing Sun and Weiquan Huang and Jiang Wu and Chenya Gu and Wei Li and Songyang Zhang and Hang Yan and Conghui He},
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year={2024},
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eprint={2403.14112},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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