小数据也能训好医学大模型，分类与实体识别效果显著
Fine-Tuning LLMs on Small Medical Datasets: Text Classification and Normalization Effectiveness on Cardiology reports and Discharge records
- 用200-300条数据微调小型模型，实现高效文本分类和实体识别
- 在心脏病报告和出院记录上，小模型性能接近大模型
- 适合临床文本结构化、医疗自动化场景的快速部署
我们研究了在小规模医学数据集上微调大型语言模型（LLMs）在文本分类和命名实体识别任务中的有效性。基于德语心脏病报告数据集和i2b2吸烟挑战数据集，实验表明，对小型模型进行本地微调，在有限训练数据下仍可提升性能，达到与大型模型相当的效果。在仅200-300个训练样本时即观察到明显性能提升。研究表明，针对特定任务微调LLMs具有潜力，可推动临床工作流自动化，并高效从非结构化医学文本中提取结构化数据。
原文摘要 · Abstract (English)
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.
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