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LLMs struggle to fully substantiate claims in clinical QA, study finds

By PulseAugur Editorial · [1 sources] ·

A new research paper titled "Verifiable by Construction" evaluates the ability of large language models (LLMs) to provide verifiable citations for clinical question answering. The study found that while most models can attach verbatim quotes to over 90% of their claims, these quotes often fail to fully substantiate the claims. For example, Claude Opus-5 produced verbatim quotes for 98.0% of its claims but only fully substantiated 37.1%. The research highlights a capability gap in LLMs for building reliable clinical QA systems. AI

IMPACT Highlights a critical gap in LLM reliability for applications requiring verifiable information, impacting trust in AI for clinical decision support.

RANK_REASON Research paper evaluating LLM capabilities on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle to fully substantiate claims in clinical QA, study finds

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Research paper evaluating LLM capabilities on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Jiashuo Zhang, Yuling Chen, Yvonne Commodore-Mensah, Michael Oberst ·

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

    arXiv:2609.15964v1 Announce Type: new 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 verif…

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