Large Language Models (LLMs) have demonstrated their efficacy across a broad spectrum of tasks in healthcare applications. However, often LLMs need to be fine-tuned on task-specific expert annotated data to achieve optimal performance, which can be expensive and time consuming. In this study, we fine-tune PaLM-2 with parameter efficient fine-tuning (PEFT) using noisy labels obtained from gemini-pro 1.0 for the detection of Schedule-of-Event (SoE) tables, which specify care plan in clinical trial protocols. We introduce a filtering mechanism to select high-confidence labels for this table classification task, thereby reducing the noise in the auto-generated labels. We show that fine-tuned PaLM-2 with those labels achieves performance that exceeds the gemini-pro 1.0 and other LLMs. Furthermore, its performance is close to a PaLM-2 fine-tuned on labels obtained from non-expert annotators. Our results show that leveraging LLM-generated labels through powerful models like gemini-pro can potentially serve as a viable strategy for improving LLM performance through fine-tuning in specialized tasks, particularly in domains where expert annotations are scarce, expensive, or time-consuming to obtain.

通过使用gemini-pro 1.0生成的噪声标签，使用参数高效的fine-tuning（PEFT）对PaLM-2进行微调，减少自动生成标签的噪声，从而在临床试验方案的Schedule-of-Event表的检测中，在超过gemini-pro 1.0和其他LLMs的性能的基础上，接近于使用非专家注释者获得的标签对PaLM-2进行微调的性能。

利用LLM标记数据进行有选择的微调可能减少对人工注释的依赖：以事件表检测为例的案例研究