用提示工程提升大模型在中医领域的表现
Intelligent Understanding of Large Language Models in Traditional Chinese Medicine Based on Prompt Engineering Framework
- 设计TCM-Prompt框架，整合多种预训练模型与提示模板
- 在疾病分类等任务中显著优于基线方法
- 适合中医AI研究者快速构建专用模型
本文探索了提示工程在提升大语言模型（LLMs）处理中医领域任务中的应用。提出TCM-Prompt框架，集成多种预训练语言模型（PLMs）、模板、分词与表述方法，使研究人员能便捷地构建和微调针对特定中医任务的模型。在疾病分类、证候识别、中药推荐及通用自然语言处理任务上进行实验，结果表明该方法相比基线方法具有明显优势。研究显示，提示工程是提升大模型在中医等专业领域表现的有力手段，具备数字化、现代化及个性化医疗的应用前景。
原文摘要 · Abstract (English)
This paper explores the application of prompt engineering to enhance the performance of large language models (LLMs) in the domain of Traditional Chinese Medicine (TCM). We propose TCM-Prompt, a framework that integrates various pre-trained language models (PLMs), templates, tokenization, and verbalization methods, allowing researchers to easily construct and fine-tune models for specific TCM-related tasks. We conducted experiments on disease classification, syndrome identification, herbal medicine recommendation, and general NLP tasks, demonstrating the effectiveness and superiority of our approach compared to baseline methods. Our findings suggest that prompt engineering is a promising technique for improving the performance of LLMs in specialized domains like TCM, with potential applications in digitalization, modernization, and personalized medicine.
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