arXiv AI By Bushi Xiao, Sarvesh Soni, Daisy Zhe Wang

Reverse Probing: Supervised Token-level Uncertainty Quantification for Large Language Models in Clinical Text

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arXiv Computation and Language
Oct 23

From Structured Records to Patient Narratives: Preserving Clinical Alignment in LLM-Generated Health Summaries

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.

By Bayram Ayadi, Florian Geissler, Johanna Schmidhuber