面向中医药大模型的知识增强方法研究OA北大核心CSTPCD

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.