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CMB: A Comprehensive Medical Benchmark in Chinese

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arxiv 2308.08833 v2 pith:2GGYX4G7 submitted 2023-08-17 cs.CL cs.AI

classification cs.CLcs.AI
keywords medicalbenchmarkchinesellmsmedicinechinacomprehensiveevaluation
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation

    cs.AI 2026-07 conditional novelty 6.5 of 10

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    ChiMed 2.0 is a 204.4M-character Chinese medical dataset spanning pretraining, SFT, and preference data that yields small gains on CMMLU and CEval medical subsets.

  3. Enterprise Large Language Model Evaluation Benchmark

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    A 14-task enterprise LLM benchmark built mostly from GPT-4o-generated labels and scored by GPT-4o-as-judge shows open-source models closing the reasoning gap, but the dataset is not public and the evaluation is partly...

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