arXiv:2608. 09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks.
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The paper introduces the first benchmark for evaluating confidence estimation in large language models during multi‑turn medical consultations, combining three types of medical data and an information sufficiency gradient to capture how confidence and correctness evolve as evidence accumulates. Experiments with 27 methods reveal that token‑level and consistency‑level confidence approaches are limited by medical data, and that medical reasoning must be judged on both diagnostic accuracy and information completeness. Building on these findings, the authors propose MedConf, a retrieval‑augmented, linguistically grounded self‑assessment framework that aligns patient information with supporting, missing, and contradictory relations, producing interpretable confidence estimates that outperform existing methods across multiple datasets and LLMs.
arXiv:2606. 07237v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in healthcare for tasks such as clinical question answering, diagnosis support, and report summarization.
The study examines how language models (LMs) alter the expressed certainty of statements when rewriting text, a process termed certainty distortion. Using an LM‑based metric aligned with human judgments, the authors find that up to 75% of LM outputs exhibit such distortion, with most models more likely to inflate certainty than reduce it. Repeated paraphrasing can amplify this effect, especially in medical contexts, and while prompt interventions help, they do not fully eliminate the bias.
arXiv:2608.22483v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly support decision-making in high-stakes domains, but they often hallucinate and express confidence that is mis...
arXiv:2606. 07951v1 Announce Type: cross Abstract: Humans increasingly turn to Language Models (LMs) in ways that shape beliefs and drive decisions, including discussing, rewriting, and summarizing information from scientific articles, news, and medical reports.
arXiv:2604.02543v2 Announce Type: replace Abstract: As vision-language models (VLMs) are increasingly deployed in clinical decision support, more than accuracy is required: knowing when to trust thei...