LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
This addresses the need for reliable medical LLM evaluation for clinicians and researchers, though it is incremental as it builds on existing benchmark methods.
The authors tackled the problem of evaluating large language models (LLMs) in medicine by creating LLMEval-Med, a benchmark with 2,996 questions from real-world clinical data, and found that it provides insights for safe deployment across 13 LLMs.
Evaluating large language models (LLMs) in medicine is crucial because medical applications require high accuracy with little room for error. Current medical benchmarks have three main types: medical exam-based, comprehensive medical, and specialized assessments. However, these benchmarks have limitations in question design (mostly multiple-choice), data sources (often not derived from real clinical scenarios), and evaluation methods (poor assessment of complex reasoning). To address these issues, we present LLMEval-Med, a new benchmark covering five core medical areas, including 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios. We also design an automated evaluation pipeline, incorporating expert-developed checklists into our LLM-as-Judge framework. Furthermore, our methodology validates machine scoring through human-machine agreement analysis, dynamically refining checklists and prompts based on expert feedback to ensure reliability. We evaluate 13 LLMs across three categories (specialized medical models, open-source models, and closed-source models) on LLMEval-Med, providing valuable insights for the safe and effective deployment of LLMs in medical domains. The dataset is released in https://github.com/llmeval/LLMEval-Med.