---
license: apache-2.0
---

**Want to fine-tune this dataset on LLaMA-Factory? Check this repository for preprocessing: [llm-merging datasets](https://github.com/Fzkuji/llm-merging/tree/main/datasets)**

(Unofficial!) I preprocessed the original data of the CMExam dataset on GitHub so that it can be visualized on huggingface. The dataset loading code for subsequent training (such as LLaMA-Factory) is in the ipynb file in the file directory.

---

This paper was presented at NeurIPS 2023, New Orleans, Louisana. See here for the [poster](conference_material/poster.pdf) and [slides](conference_material/presentation.pdf).

# Benchmarking Large Language Models on CMExam - A Comprehensive Chinese Medical Exam Dataset 

## Introduction

CMExam is a dataset sourced from the Chinese National Medical Licensing Examination. It consists of 60K+ multiple-choice questions and five additional question-wise annotations, including disease groups, clinical departments, medical disciplines, areas of competency, and question difficulty levels. Alongside the dataset, comprehensive benchmarks were conducted on representative LLMs on CMExam.

<img src="docs/example.png" alt="example" width="860">


## Dataset Statistics
|                            | Train         | Val           | Test          | Total         |
|----------------------------|---------------|---------------|---------------|---------------|
| Question                   | 54,497        | 6,811         | 6,811         | 68,119        |
| Vocab                      | 4,545         | 3,620         | 3,599         | 4,629         |
| Max Q tokens               | 676           | 500           | 585           | 676           |
| Max A tokens               | 5             | 5             | 5             | 5             |
| Max E tokens               | 2,999         | 2,678         | 2,680         | 2,999         |
| Avg Q tokens               | 29.78         | 30.07         | 32.63         | 30.83         |
| Avg A tokens               | 1.08          | 1.07          | 1.07          | 1.07          |
| Avg E tokens               | 186.24        | 188.95        | 201.44        | 192.21        |
| Median (Q1, Q3) Q tokens   | 17 (12, 32)   | 18 (12, 32)   | 18 (12, 37)   | 18 (12, 32)   |
| Median (Q1, Q3) A tokens   | 1 (1, 1)      | 1 (1, 1)      | 1 (1, 1)      | 1 (1, 1)      |
| Median (Q1, Q3) E tokens   | 146 (69, 246) | 143 (65, 247) | 158 (80, 263) | 146 (69, 247) |

\*Q: Question; A: Answer; E: Explanation

## Annotation Characteristics
|     Annotation Content     |          References         | Unique values |
|----------------------------|-----------------------------|---------------|
|       Disease Groups       | The 11th revision of ICD-11 |      27       |
|   Clinical Departments     | The Directory of Medical Institution Diagnostic and Therapeutic Categories (DMIDTC) | 36 |
|    Medical Disciplines     | List of Graduate Education Disciplinary Majors (2022) | 7 |
|   Medical Competencies     |    Medical Professionals    |       4       |
|      Difficulty Level      |       Human Performance     |       5       |

## Benchmarks

Alongside the dataset, we further conducted thorough experiments with representative LLMs and QA algorithms on CMExam.

<img src="docs/overall_comparison.jpg" alt="overall_comparison" width="860">

## Side notes

### Limitations:

- Excluding non-textual questions may introduce biases.
- BLEU and ROUGE metrics are inadequate for fully assessing explanations; better expert analysis needed in future.
- 
### Ethics in Data Collection:

- Adheres to legal and ethical guidelines.
- Authenticated and accurate for evaluating LLMs.
- Intended for academic/research use only; commercial misuse prohibited.
- Users should acknowledge dataset limitations and specific context.
- Not for assessing individual medical competence or patient diagnosis.
- 
### Future directions:

- Translate to English (in-progress)
- Include multimodal information (our new dataset ChiMed-Vision-Language-Instruction - 469,441 QA pairs: [https://paperswithcode.com/dataset/qilin-med-vl](https://paperswithcode.com/dataset/qilin-med-vl))

## Citation

Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset
https://arxiv.org/abs/2306.03030

```
@article{liu2023benchmarking,
  title={Benchmarking Large Language Models on CMExam--A Comprehensive Chinese Medical Exam Dataset},
  author={Liu, Junling and Zhou, Peilin and Hua, Yining and Chong, Dading and Tian, Zhongyu and Liu, Andrew and Wang, Helin and You, Chenyu and Guo, Zhenhua and Zhu, Lei and others},
  journal={arXiv preprint arXiv:2306.03030},
  year={2023}
}
```