Reverse Probing: Supervised Token-level Uncertainty Quantification for Large Language Models in Clinical Text
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arXiv:2609.13481v1 Announce Type: new Abstract: Large language models have made abstractive summarization remarkably fluent, but generated summaries can hallucinate facts, posing serious risks in bio...
The paper explores how Large Language Models can translate structured clinical data into patient-friendly narratives while maintaining clinical accuracy. Using a constrained generation framework that blends evidence conditioning with Retrieval-Augmented Generation, the authors evaluate alignment through Natural Language Inference, factuality metrics, and readability. Their results show that grounding constraints improve clinical alignment and uncertainty preservation across three LLMs, though they may increase reading difficulty and medical terminology.
Uncertainty estimation is essential not only for the trustworthy deployment of large language models (LLMs) but also as a foundation for self-refinement in LLM generation. However, existing approaches operate at suboptimal granularities: token-level scores lack semantic coherence, while sequence-level scores fail to localize errors.
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arXiv:2509.19375v2 Announce Type: replace-cross Abstract: Large language models are increasingly used for clinical text classification, where overconfident misclassifications can directly affect pati...