Selective Fine-tuning on LLM-labeled Data May Reduce Reliance on Human Annotation: A Case Study Using Schedule-of-Event Table Detection

arXiv:2405.06093v1 Announce Type: new
Abstract: 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 …

abstract annotated data annotation applications arxiv case case study cs.cl cs.lg data detection event expert fine-tuning healthcare however human language language models large language large language models llm llms performance reduce reliance spectrum study table table detection tasks type