CMB: A Comprehensive Medical Benchmark in Chinese
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909160619114496 |
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| author | Wang, Xidong Chen, Guiming Hardy Song, Dingjie Zhang, Zhiyi Chen, Zhihong Xiao, Qingying Jiang, Feng Li, Jianquan Wan, Xiang Wang, Benyou Li, Haizhou |
| author_facet | Wang, Xidong Chen, Guiming Hardy Song, Dingjie Zhang, Zhiyi Chen, Zhihong Xiao, Qingying Jiang, Feng Li, Jianquan Wan, Xiang Wang, Benyou Li, Haizhou |
| contents | Large Language Models (LLMs) provide a possibility to make a great breakthrough in medicine. The establishment of a standardized medical benchmark becomes a fundamental cornerstone to measure progression. However, medical environments in different regions have their local characteristics, e.g., the ubiquity and significance of traditional Chinese medicine within China. Therefore, merely translating English-based medical evaluation may result in \textit{contextual incongruities} to a local region. To solve the issue, we propose a localized medical benchmark called CMB, a Comprehensive Medical Benchmark in Chinese, designed and rooted entirely within the native Chinese linguistic and cultural framework. While traditional Chinese medicine is integral to this evaluation, it does not constitute its entirety. Using this benchmark, we have evaluated several prominent large-scale LLMs, including ChatGPT, GPT-4, dedicated Chinese LLMs, and LLMs specialized in the medical domain. We hope this benchmark provide first-hand experience in existing LLMs for medicine and also facilitate the widespread adoption and enhancement of medical LLMs within China. Our data and code are publicly available at https://github.com/FreedomIntelligence/CMB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_08833 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CMB: A Comprehensive Medical Benchmark in Chinese Wang, Xidong Chen, Guiming Hardy Song, Dingjie Zhang, Zhiyi Chen, Zhihong Xiao, Qingying Jiang, Feng Li, Jianquan Wan, Xiang Wang, Benyou Li, Haizhou Computation and Language Artificial Intelligence Large Language Models (LLMs) provide a possibility to make a great breakthrough in medicine. The establishment of a standardized medical benchmark becomes a fundamental cornerstone to measure progression. However, medical environments in different regions have their local characteristics, e.g., the ubiquity and significance of traditional Chinese medicine within China. Therefore, merely translating English-based medical evaluation may result in \textit{contextual incongruities} to a local region. To solve the issue, we propose a localized medical benchmark called CMB, a Comprehensive Medical Benchmark in Chinese, designed and rooted entirely within the native Chinese linguistic and cultural framework. While traditional Chinese medicine is integral to this evaluation, it does not constitute its entirety. Using this benchmark, we have evaluated several prominent large-scale LLMs, including ChatGPT, GPT-4, dedicated Chinese LLMs, and LLMs specialized in the medical domain. We hope this benchmark provide first-hand experience in existing LLMs for medicine and also facilitate the widespread adoption and enhancement of medical LLMs within China. Our data and code are publicly available at https://github.com/FreedomIntelligence/CMB. |
| title | CMB: A Comprehensive Medical Benchmark in Chinese |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2308.08833 |