Large Language Models (LLMs) like the GPT and LLaMA families have demonstrated exceptional capabilities in capturing and condensing critical contextual information and achieving state-of-the-art performance in the summarization task. However, community concerns about these models' hallucination issues continue to rise. LLMs sometimes generate factually hallucinated summaries, which can be extremely harmful in the clinical domain NLP tasks (e.g., clinical note summarization), where factually incorrect statements can lead to critically erroneous diagnoses. Fine-tuning LLMs using human feedback has shown the promise of aligning LLMs to be factually consistent during generation, but such training procedure requires high-quality human-annotated data, which can be extremely expensive to get in the clinical domain. In this work, we propose a new pipeline using ChatGPT instead of human experts to generate high-quality feedback data for improving factual consistency in the clinical note summarization task. We focus specifically on edit feedback because recent work discusses the shortcomings of human alignment via preference feedback in complex situations (such as clinical NLP tasks that require extensive expert knowledge), as well as some advantages of collecting edit feedback from domain experts. In addition, although GPT has reached the expert level in many clinical NLP tasks (e.g., USMLE QA), there is not much previous work discussing whether GPT can generate expert-level edit feedback for LMs in the clinical note summarization task. We hope to fill this gap. Finally, our evaluations demonstrate the potential use of GPT edits in human alignment, especially from a factuality perspective.

翻译：大型语言模型（LLMs），如GPT和LLaMA系列，在捕捉和浓缩关键上下文信息方面展现出卓越能力，并在摘要任务中取得了最先进的性能。然而，社区对这些模型幻觉问题的担忧持续加剧。LLMs有时会生成事实性错误的摘要，这在临床领域自然语言处理任务（例如临床记录摘要）中可能极其有害，因为错误的事实陈述可能导致严重误诊。使用人类反馈微调LLMs显示出在生成过程中使LLMs在事实上保持对齐的前景，但这种训练过程需要高质量的人工标注数据，这在临床领域可能极为昂贵。在本工作中，我们提出了一种新流程，使用ChatGPT而非人类专家来生成高质量反馈数据，以改善临床记录摘要任务中的事实一致性。我们特别关注编辑反馈，因为近期工作讨论了在复杂情境（如需要广泛专家知识的临床自然语言处理任务）中通过偏好反馈进行人类对齐的缺点，以及从领域专家收集编辑反馈的一些优势。此外，尽管GPT在许多临床自然语言处理任务（例如USMLE问答）中已达到专家水平，但很少有先前工作讨论GPT能否在临床记录摘要任务中为语言模型生成专家级编辑反馈。我们希望填补这一空白。最后，我们的评估证明了GPT编辑在人类对齐中的潜在用途，尤其是从事实性角度而言。