  Large Language Models (LLMs) such as GPT & 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 >100B parameter GPT variants
like GPT-3.5 & GPT-4 to act as synthetic experts to generate high-quality
synthetics feedback aimed at enhancing factual consistency in clinical note
summarization. Our research primarily focuses on edit feedback generated by
these synthetic feedback experts without additional human annotations,
mirroring and optimizing the practical scenario in which medical professionals
refine AI system outputs. Although such 100B+ parameter GPT variants have
proven to demonstrate expertise in various clinical NLP tasks, such as the
Medical Licensing Examination, there is scant research on their capacity to act
as synthetic feedback experts and deliver expert-level edit feedback for
improving the generation quality of weaker (<10B parameter) LLMs like GPT-2
(1.5B) & Llama 2 (7B) in clinical domain. So in this work, we leverage 100B+
GPT variants to act as synthetic feedback experts offering expert-level edit
feedback, that is used to reduce hallucinations and align weaker (<10B
parameter) LLMs with medical facts using two distinct alignment algorithms (DPO
& SALT), endeavoring to narrow the divide between AI-generated content and
factual accuracy. This highlights the substantial potential of LLM-based
synthetic edits in enhancing the alignment of clinical factuality.
