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license: apache-2.0 |
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**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)** |
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(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. |
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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). |
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# Benchmarking Large Language Models on CMExam - A Comprehensive Chinese Medical Exam Dataset |
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## Introduction |
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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. |
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<img src="docs/example.png" alt="example" width="860"> |
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## Dataset Statistics |
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| | Train | Val | Test | Total | |
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|----------------------------|---------------|---------------|---------------|---------------| |
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| Question | 54,497 | 6,811 | 6,811 | 68,119 | |
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| Vocab | 4,545 | 3,620 | 3,599 | 4,629 | |
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| Max Q tokens | 676 | 500 | 585 | 676 | |
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| Max A tokens | 5 | 5 | 5 | 5 | |
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| Max E tokens | 2,999 | 2,678 | 2,680 | 2,999 | |
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| Avg Q tokens | 29.78 | 30.07 | 32.63 | 30.83 | |
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| Avg A tokens | 1.08 | 1.07 | 1.07 | 1.07 | |
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| Avg E tokens | 186.24 | 188.95 | 201.44 | 192.21 | |
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| Median (Q1, Q3) Q tokens | 17 (12, 32) | 18 (12, 32) | 18 (12, 37) | 18 (12, 32) | |
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| Median (Q1, Q3) A tokens | 1 (1, 1) | 1 (1, 1) | 1 (1, 1) | 1 (1, 1) | |
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| Median (Q1, Q3) E tokens | 146 (69, 246) | 143 (65, 247) | 158 (80, 263) | 146 (69, 247) | |
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\*Q: Question; A: Answer; E: Explanation |
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## Annotation Characteristics |
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| Annotation Content | References | Unique values | |
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|----------------------------|-----------------------------|---------------| |
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| Disease Groups | The 11th revision of ICD-11 | 27 | |
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| Clinical Departments | The Directory of Medical Institution Diagnostic and Therapeutic Categories (DMIDTC) | 36 | |
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| Medical Disciplines | List of Graduate Education Disciplinary Majors (2022) | 7 | |
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| Medical Competencies | Medical Professionals | 4 | |
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| Difficulty Level | Human Performance | 5 | |
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## Benchmarks |
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Alongside the dataset, we further conducted thorough experiments with representative LLMs and QA algorithms on CMExam. |
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<img src="docs/overall_comparison.jpg" alt="overall_comparison" width="860"> |
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## Side notes |
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### Limitations: |
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- Excluding non-textual questions may introduce biases. |
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- BLEU and ROUGE metrics are inadequate for fully assessing explanations; better expert analysis needed in future. |
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### Ethics in Data Collection: |
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- Adheres to legal and ethical guidelines. |
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- Authenticated and accurate for evaluating LLMs. |
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- Intended for academic/research use only; commercial misuse prohibited. |
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- Users should acknowledge dataset limitations and specific context. |
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- Not for assessing individual medical competence or patient diagnosis. |
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### Future directions: |
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- Translate to English (in-progress) |
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- 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)) |
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## Citation |
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Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset |
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https://arxiv.org/abs/2306.03030 |
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``` |
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@article{liu2023benchmarking, |
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title={Benchmarking Large Language Models on CMExam--A Comprehensive Chinese Medical Exam Dataset}, |
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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}, |
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journal={arXiv preprint arXiv:2306.03030}, |
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year={2023} |
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} |
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``` |
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