Abstract
We investigate the effectiveness of fine-tuning large language models (LLMs) on small medical datasets for text classification and named entity recognition tasks. Using a German cardiology report dataset and the i2b2 Smoking Challenge dataset, we demonstrate that fine-tuning small LLMs locally on limited training data can improve performance achieving comparable results to larger models. Our experiments show that fine-tuning improves performance on both tasks, with notable gains observed with as few as 200-300 training examples. Overall, the study highlights the potential of task-specific fine-tuning of LLMs for automating clinical workflows and efficiently extracting structured data from unstructured medical text.
Abstract (translated)
我们研究了在小型医疗数据集上对大型语言模型（LLMs）进行微调，以改进文本分类和命名实体识别任务的有效性。通过使用德国心脏病报告数据集和i2b2吸烟挑战数据集，我们展示了在有限训练数据下本地微调小规模的LLM可以提高性能，并且能达到与更大模型相当的结果。我们的实验表明，在这两种任务上进行微调都可以改善性能，即使使用少至200-300个训练样本也观察到了显著的提升效果。总体而言，该研究强调了针对特定任务对大型语言模型进行微调在自动化临床工作流程和高效提取结构化医疗文本数据方面的潜力。
URL
https://arxiv.org/abs/2503.21349
