Large Language Models (LLMs) such as GPT and Llama have demonstrated
significant achievements in summarization tasks but struggle with factual
inaccuracies, a critical issue in clinical NLP applications where errors could
lead to serious consequences. To counter the high costs and limited
availability of expert-annotated data for factual alignment, this study
introduces an innovative pipeline that utilizes GPT-3.5 and GPT-4 to generate
high-quality feedback aimed at enhancing factual consistency in clinical note
summarization. Our research primarily focuses on edit feedback, mirroring the
practical scenario in which medical professionals refine AI system outputs
without the need for additional annotations. Despite GPT's proven expertise in
various clinical NLP tasks, such as the Medical Licensing Examination, there is
scant research on its capacity to deliver expert-level edit feedback for
improving weaker LMs or LLMs generation quality. This work leverages GPT's
advanced capabilities in clinical NLP to offer expert-level edit feedback.
Through the use of two distinct alignment algorithms (DPO and SALT) based on
GPT edit feedback, our goal is to reduce hallucinations and align closely with
medical facts, endeavoring to narrow the divide between AI-generated content
and factual accuracy. This highlights the substantial potential of GPT edits in
enhancing the alignment of clinical factuality.

本研究利用 GPT-3.5 和 GPT-4 生成高质量的反馈，以提高临床笔记摘要的事实一致性，主要关注医学专业人员优化人工智能系统输出的实际场景，通过两种不同的对齐算法（DPO 和 SALT）基于 GPT 的编辑反馈，致力于减少幻觉并与医学事实紧密对齐，突显了 GPT 编辑在提高临床信息准确性方面的巨大潜力。