[2605.01011] CLEAR: Revealing How Noise and Ambiguity Degrade Reliability in LLMs for Medicine
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arXiv:2605.01011 (cs)
[Submitted on 1 May 2026]
Title:CLEAR: Revealing How Noise and Ambiguity Degrade Reliability in LLMs for Medicine
Authors:Kevin H. Guo, Chao Yan, Avinash Baidya, Katherine Brown, Xiang Goa, Juming Xiong, Zhijun Yin, Bradley A. Malin
View a PDF of the paper titled CLEAR: Revealing How Noise and Ambiguity Degrade Reliability in LLMs for Medicine, by Kevin H. Guo and 7 other authors
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Abstract:Medical large language model (LLM) evaluations rely on simplified, exam-style benchmarks that rarely reflect the ambiguity of real-world medical inquiries. We introduce the CLinical Evaluation of Ambiguity and Reliability (CLEAR) framework, which assesses how decision-space presentation, ambiguity, and uncertainty affect LLMs' reasoning on medical benchmarks. CLEAR systematically perturbs (1) the number of plausible answer options, (2) the presence of a ground truth or abstention option, and (3) the semantic framing of answer options. Applying CLEAR on three benchmarks evaluated across 17 LLMs reveals three notable limitations of existing evaluation methods. First, increasing the number of plausible answers degrades a model's ability to identify the correct answer and abstain against incorrect ones. Second, this lack of caution intensifies as the framing of abstention shifts from assertive rejection like "None of the Above" to uncertainty admission like "I don't know" (IDK). Notably, just including IDK in the answer space increases incorrect answer selections. Lastly, we formalize the performance gap between identifying the correct answer and abstaining from incorrect ones as the humility deficit, which worsens with model scale. Our findings reveal limitations in standard medical benchmarks and underscore that scaling alone does not resolve LLM reliability issues.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.01011 [cs.CL]
(or arXiv:2605.01011v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.01011
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kevin Guo [view email] [v1] Fri, 1 May 2026 18:23:24 UTC (4,891 KB)
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