In acupuncture therapy, the accurate location of acupoints is essential for its effectiveness. The advanced language understanding capabilities of large language models (LLMs) like Generative Pre-trained Transformers (GPT) present a significant opportunity for extracting relations related to acupoint locations from textual knowledge sources. This study aims to compare the performance of GPT with traditional deep learning models (Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT)) in extracting acupoint-related location relations and assess the impact of pretraining and fine-tuning on GPT's performance. We utilized the World Health Organization Standard Acupuncture Point Locations in the Western Pacific Region (WHO Standard) as our corpus, which consists of descriptions of 361 acupoints. Five types of relations ('direction_of,' 'distance_of,' 'part_of,' 'near_acupoint,' and 'located_near') (n= 3,174) between acupoints were annotated. Five models were compared: BioBERT, LSTM, pre-trained GPT-3.5, and fine-tuned GPT-3.5, as well as pre-trained GPT-4. Performance metrics included micro-average exact match precision, recall, and F1 scores. Our results demonstrate that fine-tuned GPT-3.5 consistently outperformed other models in F1 scores across all relation types. Overall, it achieved the highest micro-average F1 score of 0.92. This study underscores the effectiveness of LLMs like GPT in extracting relations related to acupoint locations, with implications for accurately modeling acupuncture knowledge and promoting standard implementation in acupuncture training and practice. The findings also contribute to advancing informatics applications in traditional and complementary medicine, showcasing the potential of LLMs in natural language processing.

这项研究比较了用于提取与穴位位置相关关系的传统深度学习模型（如LSTM和BioBERT）与大型语言模型（LLM）GPT的性能，并评估预训练和微调对GPT性能的影响。结果显示，微调的GPT-3.5在所有关系类型上一直表现优于其他模型，整体上达到了最高的微平均F1得分为0.92。研究强调了像GPT这样的LLM在提取与穴位位置相关的关系方面的有效性，对准确建模针灸知识并促进其在针灸培训和实践中的标准化实施具有重要意义。该研究还有助于推动传统和综合医学中的信息学应用，展示了LLM在自然语言处理中的潜力。

利用大型语言模型进行关系提取：以针灸穴位定位为案例研究