Quantifying and Mitigating Premature Closure in Frontier LLMs

• URL: http://arxiv.org/abs/2605.15000v1
• Date: Thu, 14 May 2026 16:02:28 GMT
• Title: Quantifying and Mitigating Premature Closure in Frontier LLMs
• Abstract summary: Premature closure is a recognized contributor to diagnostic error but remains underexamined in large language models (LLMs)<n>We evaluated five frontier LLMs across structured and open-ended medical tasks.
• Score: 4.513932256095551
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Premature closure, or committing to a conclusion before sufficient information is available, is a recognized contributor to diagnostic error but remains underexamined in large language models (LLMs). We define LLM premature closure as inappropriate commitment under uncertainty: providing an answer, recommendation, or clinical guidance when the safer response would be clarification, abstention, escalation, or refusal. We evaluated five frontier LLMs across structured and open-ended medical tasks. In MedQA (n = 500) and AfriMed-QA (n = 490) questions where the correct choice had been removed, models still selected an answer at high rates, with baseline false-action rates of 55-81% and 53-82%, respectively. In open-ended evaluation, models gave inappropriate answers on an average of 30% of 861 HealthBench questions and 78% of 191 physician-authored adversarial queries. Safety-oriented prompting reduced premature closure across models, but residual failure persisted, highlighting the need to evaluate whether medical LLMs know when not to answer.

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