计算机科学与探索2024，Vol.18Issue(10)：2616-2629,14.DOI:10.3778/j.issn.1673-9418.2407082
面向中医药大模型的知识增强方法研究
Knowledge Augmentation on Traditional Chinese Medicine Language Model
摘要
Abstract
Recently,large language models(LLM)have made significant achievements in various fields.However,due to lack of specialized knowledge and the gap between modern medicine and traditional Chinese medicine(TCM),it is still a challenge to deploy LLM in TCM.Existing methods fail to maintain the structure of TCM pre-scription.To address the problems,a pattern of knowledge augmentation is proposed.The method includes model training,knowledge graph construction and knowledge augmentation.In the training phase,TCM language model is trained on TCM corpus,by a two-stage method combining pre-training and fine-tuning.In the knowledge graph con-struction phase,prescription knowledge graph is constructed from nearly 100000 preprocessed classical TCM pre-scriptions and those from ancient books.In the knowledge augmentation phase,enhanced by the above pattern,out-puts are generated from computation of knowledge graph,according to the schema of knowledge graph from search-ing result,which preserves the structure of prescriptions.A set of evaluations specific to prescription optimizations is proposed,including objective and subjective indicators,to evaluate the performance of the model for the task.Ex-periment shows that the model improves greatly on both subjective and objective evaluations compared with base-lines.BLEU-1 is increased by up to 0.09,while ROUGE-1 is increased by up to 0.21.Ablation study shows that,it is of vital importance for the model performance to be knowledge-augmented.BLEU-1 of augmentation-free model is decreased by about 37%compared with that of the augmented model.关键词
大语言模型(LLM)/中医药/方剂优化/检索增强生成Key words
large language model(LLM)/traditional Chinese medicine/prescription optimization/retrieval aug-mented generation分类
信息技术与安全科学引用本文复制引用
吉祥宇,王鑫,张鹤译,孟昭鹏,张俊华,庄朋伟,贾勇哲,徐大为..面向中医药大模型的知识增强方法研究[J].计算机科学与探索,2024,18(10):2616-2629,14.基金项目
国家自然科学基金面上项目(61972275).This work was supported by the National Natural Science Foundation of China(61972275). （61972275）
