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可通过结构验证:临床问题回答中逐字引用的索赔级评估

原文标题 · Verifiable by Construction: Claim-Level Evaluation of Verbatim Citation in Clinical Question Answering

作者:张佳硕、陈玉玲、Yvonne Commodore-Mensah|大型语言模型(LLM)已被广泛用于临床问题回答(QA)。当前的系统可以在答案上附加引用,但这些引用通常指向宽泛的文本,导致时间紧迫的临床医生无法有效地验证它们。另一种选择是确保响应可以通过结构进行验证:从包含的参考材料中提供细粒度的逐字引用

来源:arXiv cs.AI/CL/LG · arxiv.org

正文为英文,可一键机器翻译(仅首次需要等待)

Computer Science > Computation and Language

Title: Verifiable by Construction: Claim-Level Evaluation of Verbatim Citation in Clinical Question Answering

Abstract: Large language models (LLMs) have been widely adopted for clinical question answering (QA). Current systems can attach citations to their answers, but these often point to broad texts, leaving time-pressed clinicians unable to verify them efficiently. An alternative is to ensure that responses are verifiable by construction: providing fine-grained verbatim quotes from reference material that substantiate claims, so users can verify an answer without opening other documents. In this paper, we evaluate the ability of current models to perform this task end-to-end: from providing citations for every factual claim, to producing verbatim quotes, to ensuring that those quotes fully substantiate the claims. To do so, we build a standardized harness over four clinical practice guidelines and evaluate twelve LLMs on 222 synthetic clinical questions, measuring each of these stages separately. We find that most models can attach verbatim quotes to over 90% of their claims from prompting alone, apart from some lightweight models such as claude-haiku-4.5. Yet these quotes often fail to substantiate every detail of the claims they accompany. For instance, claude-opus-5 produces verbatim quotes for 98.0% of its claims, but fully substantiates only 37.1%. Our work provides insights into the current capability gap of LLMs in building verifiable clinical QA systems, along with artifacts for future research.

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