Automated Rubrics for Reliable Evaluation of Medical Dialogue Systems

Abstract

Large Language Models (LLMs) are increasingly used for clinical decision support, where hallucinations and unsafe suggestions may pose direct risks to patient safety. These risks are hard to assess: subtle clinical errors are often missed by generic metrics and LLM judges using general criteria, while expert-authored fine-grained rubrics are expensive and difficult to scale. In this paper, we propose a retrieval-augmented multi-agent framework designed to automate the generation of instance-specific evaluation rubrics. Our approach grounds evaluation in authoritative medical evidence by decomposing retrieved content into atomic facts and synthesizing them with user interaction constraints to form verifiable, fine-grained evaluation criteria. Evaluated on HealthBench and LLMEval-Med datasets, our framework achieves Clinical Intent Alignment (CIA) scores of 50.20% and 31.90%, significantly outperforming the GPT-4o baseline and demonstrating robust cross-lingual generalization. In discriminative tests on HealthBench, our rubrics yield a 7.8% higher win rate than GPT-4o baseline with nearly double score $Δ$, while ablation studies confirm its structural necessity. Beyond evaluation, our rubrics effectively guide response refinement, improving quality by 9.2%. This provides a scalable, cross-lingual foundation for both evaluating and improving medical LLMs. The code is available at https://github.com/AmbeChen/Automated-Rubric-Generation.