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。

SYNFAC-EDIT：临床摘要的事实对齐的合成模仿编辑反馈

SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in  Clinical Summarization

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.

使用 ChatGPT 生成高质量反馈数据，从而改善临床笔记概括任务中的事实一致性，并展示了 GPT 编辑在人类对齐中的潜在用途，特别是从事实角度。

临床摘要中事实对齐的合成模仿编辑反馈

Synthetic Imitation Edit Feedback for Factual Alignment in Clinical  Summarization

Electronic health records (EHRs) store an extensive array of patient
information, encompassing medical histories, diagnoses, treatments, and test
outcomes. These records are crucial for enabling healthcare providers to make
well-informed decisions regarding patient care. Summarizing clinical notes
further assists healthcare professionals in pinpointing potential health risks
and making better-informed decisions. This process contributes to reducing
errors and enhancing patient outcomes by ensuring providers have access to the
most pertinent and current patient data. Recent research has shown that
incorporating prompts with large language models (LLMs) substantially boosts
the efficacy of summarization tasks. However, we show that this approach also
leads to increased output variance, resulting in notably divergent outputs even
when prompts share similar meanings. To tackle this challenge, we introduce a
model-agnostic Soft Prompt-Based Calibration (SPeC) pipeline that employs soft
prompts to diminish variance while preserving the advantages of prompt-based
summarization. Experimental findings on multiple clinical note tasks and LLMs
indicate that our method not only bolsters performance but also effectively
curbs variance for various LLMs, providing a more uniform and dependable
solution for summarizing vital medical information.

使用软提示的模型无关软校准 (SPec) 管道可降低输出方差，同时保留提示式摘要的优点，并有效增强大型语言模型的性能，为临床笔记的摘要提供了更可靠的解决方案。