Hallucination Detection-Guided Preference Optimization for Clinical Summarization 事件

Hallucination Detection-Guided Preference Optimization for Clinical Summarization arXiv:2605.28910v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications. We introduce \itermodelfull (\itermodel), an inference-time method that leverages hallucination detectors to guide iterative summary revisions