# CMLM-ZhongJing A Traditional Chinese Medicine language model, inspired by the wisdom of the eminent representative of ancient Chinese medical scholars, Zhang Zhongjing. This model aims to illuminate the profound knowledge of Traditional Chinese Medicine, bridging the gap between ancient wisdom and modern technology, and providing a reliable and professional tool for the medical and legal fields. However, all generated results are for reference only and should be provided by experienced professionals for diagnosis and treatment results and suggestions. ## Instruction Data Construction While many works such as Alpaca, Belle, etc., are based on the self-instruction approach which effectively harnesses the knowledge of large language models to generate diverse and creative instructions, this approach may lead to noise in instruction data, thereby affecting the accuracy of the model in fields where professional knowledge has a low tolerance for errors, such as medical and legal scenarios. Therefore, how to quickly invoke the OpenAI API without sacrificing the professionalism of instruction data has become an important research direction for instruction data construction and annotation scenarios. Here, we will briefly describe our preliminary experimental exploration. 1.1 Multi-task Therapeutic Behavior Decomposition Instruction Construction Strategy Human memory and understanding require the construction of various scenarios and stories to implicitly encode knowledge information. The clarity of memory depends on the duration and richness of the learning process. Interleaved learning, spaced practice, and diversified learning can enhance the consolidation of knowledge, thereby forming a deep understanding of domain knowledge. Our approach is to learn from the process of human memory knowledge, use professional tables, leverage the language representation capabilities of large language models, strictly set specific prompt templates, so that the model can generate 16 scenarios based on the table data of Chinese medicine gynecology prescriptions, including patient therapeutic story, diagnostic analysis, diagnosis treatment expected result, formula function, interactive story, patient therapeutic story, narrative medicine, tongue & pulse, therapeutic template making, critical thinking, follow up, prescription, herb dosage, case study, real-world problem, disease mechanism, etc., to promote the model's reasoning ability for prescription data and diagnostic thinking logic. 1.2 Regular Instruction Data Construction Strategy In addition, we have also added instructions based on the content of Chinese medicine ancient books, noun explanations, symptom synonyms, antonyms, syndromes, symptoms, treatment methods, etc. In order to form a control experiment, we only use one instruction template to represent data for this part, and the number of this part of the data is 80,000, which is significantly more than the number of instructions constructed by the above strategy. The following is the specific number of instructions and tokens information. Data Source and Instruction Quantity Table: File Name Total Tokens Quantity Input Quantity Instruction Quantity Output Quantity patient_therapeutic_story_data1.json 62722 208 208 208 diagnostic_analysis.json 1492105 6592 6592 6592 formula_funtion_data.json 100533 2115 2115 2115 diagnosis_treatment_expected_result_formatted_... 33822 153 153 153 中医词典.json 2188672 20376 20376 20376 反义词.json 272 9 9 9 互动故事instructed_data.json 55262 219 219 219 patient_therapeutic_story_data3.json 50785 660 660 660 证候名词解释.json 67443 976 976 976 narrative_medicine_formatted_data.json 61336 213 213 213 中医症状同义词.json 1515796 27650 27650 27650 近义词2.json 111186 2217 2217 2217 tongue_palse.json 328597 3723 3723 3723 therapeutic_template_making.json 335602 4929 4929 4929 patient_therapeutic_story_data2.json 50785 660 660 660 critical_thinking_data.json 31502 229 229 229 follow_up_data.json 504717 5990 5990 5990 prescription_data.json 107694 2898 2898 2898 herb_dosage.json 564394 5973 5973 5973 case_study_data.json 58319 243 243 243 妇科近义词.json 29740 543 543 543 real_world_problem.json 1493551 7990 7990 7990 disease_mechanism.json 997377 8024 8024 8024 治法名词解释data_cleaned.json 81211 1123 1123 1123 Total 26294720 135108 135108 135108