作者位次 #2/4ACL 2026Findings· Findings of ACL
M ed S core: Generalizable Factuality Evaluation of Open-ended Long-form Medical Answers by Domain-adapted Claim Decomposition and Verification
Heyuan Huang · Alexandra DeLucia · Vijay Murari Tiyyala · Mark Dredze
摘要
While Large Language Models (LLMs) can generate fluent and convincing responses, they are not necessarily correct. This is especially apparent in the popular decompose-then-verify factuality evaluation pipeline, where LLMs evaluate generated text by decomposing it into individual, valid claims. F...