README.md

# 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