# CHARM✨ Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations [ACL2024]
[](https://arxiv.org/abs/2403.14112)
[](./LICENSE)
📃[Paper](https://arxiv.org/abs/2403.14112)
🏰[Project Page](https://opendatalab.github.io/CHARM/)
🏆[Leaderboard](https://opendatalab.github.io/CHARM/leaderboard.html)
✨[Findings](https://opendatalab.github.io/CHARM/findings.html)
## 数据集介绍
**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。
## 与其他常识推理评测基准的比较
## 🛠️ 如何使用
以下是快速下载 CHARM 并在 OpenCompass 上进行评估的步骤。
### 1. 下载 CHARM
```bash
git clone https://github.com/opendatalab/CHARM ${path_to_CHARM_repo}
cd ${path_to_opencompass}
mkdir data
ln -snf ${path_to_CHARM_repo}/data/CHARM ./data/CHARM
```
### 2. 推理和评测
```bash
cd ${path_to_opencompass}
# 修改配置文件`configs/eval_charm_rea.py`: 将现有的模型取消注释，或者添加你想评测的模型
python run.py configs/eval_charm_rea.py -r --dump-eval-details
# 修改配置文件`configs/eval_charm_mem.py`: 将现有的模型取消注释，或者添加你想评测的模型
python run.py configs/eval_charm_mem.py -r --dump-eval-details
```
推理和评测的结果位于路径`${path_to_opencompass}/outputs`, 如下所示:
```bash
outputs
├── CHARM_mem
│ └── chat
│ └── 20240605_151442
│ ├── predictions
│ │ ├── internlm2-chat-1.8b-turbomind
│ │ ├── llama-3-8b-instruct-lmdeploy
│ │ └── qwen1.5-1.8b-chat-hf
│ ├── results
│ │ ├── internlm2-chat-1.8b-turbomind_judged-by--GPT-3.5-turbo-0125
│ │ ├── llama-3-8b-instruct-lmdeploy_judged-by--GPT-3.5-turbo-0125
│ │ └── qwen1.5-1.8b-chat-hf_judged-by--GPT-3.5-turbo-0125
│ └── summary
│ └── 20240605_205020 # MEMORY_SUMMARY_DIR
│ ├── judged-by--GPT-3.5-turbo-0125-charm-memory-Chinese_Anachronisms_Judgment
│ ├── judged-by--GPT-3.5-turbo-0125-charm-memory-Chinese_Movie_and_Music_Recommendation
│ ├── judged-by--GPT-3.5-turbo-0125-charm-memory-Chinese_Sport_Understanding
│ ├── judged-by--GPT-3.5-turbo-0125-charm-memory-Chinese_Time_Understanding
│ └── judged-by--GPT-3.5-turbo-0125.csv # MEMORY_SUMMARY_CSV
└── CHARM_rea
└── chat
└── 20240605_152359
├── predictions
│ ├── internlm2-chat-1.8b-turbomind
│ ├── llama-3-8b-instruct-lmdeploy
│ └── qwen1.5-1.8b-chat-hf
├── results # REASON_RESULTS_DIR
│ ├── internlm2-chat-1.8b-turbomind
│ ├── llama-3-8b-instruct-lmdeploy
│ └── qwen1.5-1.8b-chat-hf
└── summary
├── summary_20240605_205328.csv # REASON_SUMMARY_CSV
└── summary_20240605_205328.txt
```
### 3. 生成分析结果
```bash
cd ${path_to_CHARM_repo}
# 生成论文中的Table5, Table6, Table9 and Table10，详见https://arxiv.org/abs/2403.14112
PYTHONPATH=. python tools/summarize_reasoning.py ${REASON_SUMMARY_CSV}
# 生成论文中的Figure3 and Figure9，详见https://arxiv.org/abs/2403.14112
PYTHONPATH=. python tools/summarize_mem_rea.py ${REASON_SUMMARY_CSV} ${MEMORY_SUMMARY_CSV}
# 生成论文中的Table7, Table12, Table13 and Figure11，详见https://arxiv.org/abs/2403.14112
PYTHONPATH=. python tools/analyze_mem_indep_rea.py data/CHARM ${REASON_RESULTS_DIR} ${MEMORY_SUMMARY_DIR} ${MEMORY_SUMMARY_CSV}
```
## 🖊️ 引用
```bibtex
@misc{sun2024benchmarking,
title={Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations},
author={Jiaxing Sun and Weiquan Huang and Jiang Wu and Chenya Gu and Wei Li and Songyang Zhang and Hang Yan and Conghui He},
year={2024},
eprint={2403.14112},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```