English(EN) Dissociating Answerability from Extraction in LLM Question Answering over Radiology Reports

新的LLM评估方法将放射学报告中的可回答性与提取能力分开

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一种新的评估方法，用于大型语言模型（LLM）在放射学报告上的问答，旨在将可回答性与提取能力分离开来。该方法将错误回答率和过度回避率与传统的F1分数分开，从而提供对模型性能更细致的理解。当应用于13个LLM时，该评估揭示了具有相似F1分数的模型之间错误回答率的显著差异，并表明提供一个无法回答的示例可以提高回避率，而不会损害提取质量。 AI

影响这种新的评估方法可能导致在医学影像分析等专业领域中对LLM性能进行更可靠的评估。

排序理由该项目是一篇提出LLM新评估方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Priyanshu Sinha, Kelsey McDonald · 2026-10-15 04:00

在大型语言模型放射报告问答中将答案可回答性与提取性分离

arXiv:2610.17576v1 Announce Type: cross Abstract: Answering questions from radiology reports involves two decisions: whether the report contains an answer and which span gives it. A false answer presents an unsupported claim as fact; an over-abstention hides what the report says.…

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在大型语言模型放射报告问答中将答案可回答性与提取性分离

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