Abstract
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.
Abstract (translated)
本文探讨了将提示工程应用于提高大型语言模型（LLMs）在中医药（TCM）领域性能的应用。我们提出了TCM-Prompt框架，该框架整合了各种预训练语言模型（PLMs）、模板、分词和具体化方法，使研究人员能够轻松构建并调整用于特定中医药相关任务的模型。我们在疾病分类、症候识别、草药推荐以及一般自然语言处理任务上进行了实验，结果展示了我们方法的有效性和优越性，相较于基线方法更有优势。我们的研究发现表明，在中医药等专业领域中，提示工程是一种有望提高大型语言模型性能的技术，并且在数字化、现代化和个性化医疗方面具有潜在应用价值。
URL
https://arxiv.org/abs/2410.19451
