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.

翻译：本文探讨了如何应用提示工程来提升大型语言模型在传统中医领域的性能。我们提出了TCM-Prompt框架，该框架整合了多种预训练语言模型、模板、分词和词汇化方法，使研究人员能够轻松构建并针对特定中医相关任务微调模型。我们在疾病分类、证候识别、草药推荐及通用自然语言处理任务上进行了实验，结果表明，相较于基线方法，我们的方法具有显著的有效性和优越性。我们的研究表明，提示工程是提升大型语言模型在中医等专业领域性能的一种有效技术，在数字化、现代化及个性化医疗方面具有潜在的应用前景。