对抗幻觉：垂直领域中大语言模型的应用策略探讨一以中医知识问答领域为例

Objective] This paper aims to analyze the differences in combating hallucinations in large language models between unstructured knowledge exemplified by knowledge base resources and structured knowledge exemplified by knowledge graph resources, using the Traditional Chinese Medicine (TCM) Q&A domain as a case study. It further discusses strategies to enhance the capability of large language models to combat hallucinations in vertical domains based on these findings.[Methods] The study designed experiments using external knowledge combined with prompt engineering techniques to analyze the differences in prompting effects between knowledge base resources and knowledge graph resources in the TCM Q&A domain. It also explores the superiority of dynamic triplet strategies and integrated fine-tuning strategies in optimizing large language models against hallucinations.[Results] Experimental results show that compared to prompts from unstructured knowledge in the knowledge base, prompts from structured knowledge in the knowledge graph perform better in terms of accuracy, recall, and F1 score, improving by 1.9%, 2.42%, and 2.2% respectively, reaching 71.44%, 60.76%, and 65.31%. Further analysis of optimization strategies revealed that the combination of dynamic triplet strategy and fine-tuning yielded the best effects against hallucinations, achieving accuracy, recall, and F1 scores of 72.47%, 65.87%, and 68.62%, respectively.[Limitations] This study is limited to a single field, having been tested only in the domain of Traditional Chinese Medicine Q&A, and its generalizability needs to be validated in a broader range of scientific fields.[Conclusions] This study has demonstrated that in the field of Traditional Chinese Medicine, structured knowledge from knowledge graphs surpasses traditional unstructured knowledge in reducing hallucinations and enhancing the accuracy of model responses. It reveals the critical role of structured knowledge in boosting model comprehension abilities; the integration of fine-tuning strategies with knowledge resources provides an effective pathway for performance enhancement in large language models. This paper provides theoretical justification and methodological support for integrating external knowledge into large language models to enhance knowledge services.