测试大模型在临床数值推理中的鲁棒性，发现格式变化会显著影响表现。
How Robust Are Large Language Models for Clinical Numeracy? An Empirical Study on Numerical Reasoning Abilities in Clinical Contexts
- 构建1624个临床数值任务，涵盖四类推理：数值提取、计算、比较、聚合。
- 模型在数值提取上准确率超85%，但比较和聚合低于15%，微调后性能下降超30%。
- 首次在真实病历风格下评估模型鲁棒性，适合医疗AI安全研究者使用。
大型语言模型（LLMs）在临床问答与决策支持中日益受到关注，但安全部署需可靠处理异构临床记录中的患者测量数据。现有评估对临床数值推理的覆盖有限，主要集中在算术运算，且很少检验模型在不同病历格式下的鲁棒性。本文提出ClinicNumRobBench基准，包含1,624个上下文-问题实例及真值答案，评估四类临床数值能力：数值提取、算术计算、关系比较和聚合。为测试鲁棒性，该基准以三种语义等价形式呈现纵向MIMIC-IV生命体征数据，包括基于Open Patients数据集的真实病历风格变体，并使用42种问题模板生成查询。在17个LLMs上的实验表明，数值提取表现良好，多数模型准确率超过85%；而关系比较和聚合仍具挑战，部分模型得分低于15%。医学数据微调使数值推理性能较基线模型下降超过30%，且在病历风格变化下性能下降表明模型对格式敏感。ClinicNumRobBench为临床可靠数值推理提供严格测试平台。代码与数据已公开于https://github.com/MinhVuong2000/ClinicNumRobBench。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly being explored for clinical question answering and decision support, yet safe deployment critically requires reliable handling of patient measurements in heterogeneous clinical notes. Existing evaluations of LLMs for clinical numerical reasoning provide limited operation-level coverage, restricted primarily to arithmetic computation, and rarely assess the robustness of numerical understanding across clinical note formats. We introduce ClinicNumRobBench, a benchmark of 1,624 context-question instances with ground-truth answers that evaluates four main types of clinical numeracy: value retrieval, arithmetic computation, relational comparison, and aggregation. To stress-test robustness, ClinicNumRobBench presents longitudinal MIMIC-IV vital-sign records in three semantically equivalent representations, including a real-world note-style variant derived from the Open Patients dataset, and instantiates queries using 42 question templates. Experiments on 17 LLMs show that value retrieval is generally strong, with most models exceeding 85% accuracy, while relational comparison and aggregation remain challenging, with some models scoring below 15%. Fine-tuning on medical data can reduce numeracy relative to base models by over 30%, and performance drops under note-style variation indicate LLM sensitivity to format. ClinicNumRobBench offers a rigorous testbed for clinically reliable numerical reasoning. Code and data URL are available on https://github.com/MinhVuong2000/ClinicNumRobBench.
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