---
license: mit
task_categories:
- question-answering
language:
- zh
tags:
- medical
- tcm
- traditional Chinese medicine
- eval
- benchmark
- test
---

# Description

This dataset can be used to evaluate the capabilities of large language models in traditional Chinese medicine and contains multiple-choice, multiple-answer, and true/false questions.

# Changelog

- **2024-08-28: Added 7226 questions.**
- 2024-08-09: The benchmark code is available at https://github.com/huangxinping/HWTCMBench.
- 2024-08-02: System prompts are removed to ensure the purity of the evaluation results.
- 2024-07-20: Debut.


## Examples

multiple-answers questions（多选题）
```json
[
  {
    "instruction": "便秘的预防调护应注意\nA.保持心情舒畅\nB.少吃辛辣刺激性食物\nC.适当摄入油脂\nD.积极治疗肛门直肠疾病\nE.按时登厕",
    "input": "",
    "output": "ABCDE"
  }
]
```

multiple-choice questions（单选题）
```json
[
  {
    "instruction": "患者，男，50岁。眩晕欲仆，头摇而痛，项强肢颤，腰膝疫软，舌红苔薄白，脉弦有力。其病机是\nA．肝阳上亢\nB．肝肾阴虚\nC．肝阳化风\nD．阴虚风动\nE．肝血不足",
    "input": "",
    "output": "C"
  }
]
```

True/False questions（判断题）
```json
[
  {
    "instruction": "秦医医和提出了“六气病源说”。",
    "input": "",
    "output": "正确"
  },
  {
    "instruction": "中风中经络邪盛时也可出现神志改变",
    "input": "",
    "output": "错误"
  }
]
```

## Evaluation

|   | multiple-choice questions  | multiple-answers questions   | True/False questions | 
|---|---|---|---|
| llama3:8b  | 21.94%  | 17.71%  | 46.56%  |
| phi3:14b-instruct  | 26.93%  | 1.04%  | 38.93%  |
| aya:8b  | 17.85%  | 1.04%  | 34.35%  |
| mistral:7b-instruct  | 21.76%  | 2.08%  | **48.09%**  |
| qwen1.5-7b-chat  | 51.35%  | 13.54%  | 46.56%  |
| qwen1.5-14b-chat | 69.94%  | **78.12%**  | 31.30%  |
| huangdi-13b-chat | 21.73%  | 45.83%  | 0.00%  |
| canggong-14b-chat(SFT)<br>**Ours** | 55.98%  | 4.17%  | 23.66%  |
| canggong-14b-chat(DPO)<br>**Ours** | **72.33%**  | 2.08%  | 45.80%  |



> Canggong-14b-chat is an LLM of traditional Chinese medicine still in training.

## Citation
If you find this project useful in your research, please consider cite:
```
@misc{hwtcm2024,
    title={{hwtcm} a traditional Chinese medicine QA dataset for evaluating large language models},
    author={Haiwei AI Team},
    howpublished = {\url{https://huggingface.co/datasets/Monor/hwtcm}},
    year={2024}
}
```