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arXiv Computation and Language By Yinzhu Chen, Abdine Maiga, Hossein A. Rahmani, Emine Yilmaz

Retrieval-Augmented Agentic Rubric Generation for Reliable Medical Response Evaluation

Read the original on arXiv Computation and Language →

The paper introduces a retrieval‑augmented multi‑agent framework that automatically generates instance‑specific evaluation rubrics for medical language models. By retrieving authoritative medical evidence, decomposing it into atomic facts, and combining these with user interaction constraints, the system produces fine‑grained criteria that outperform GPT‑4o on HealthBench and LLMEval‑Med. The generated rubrics also guide response refinement, improving medical LLM output quality by 9.2%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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