A Traditional Chinese Medicine large 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.
## 1.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.
While many works such as Alpaca, Belle, etc., are based on the self-instruct 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.
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Adopt a multi-task therapeutic decomposition strategy, based on multidisciplinary data such as internal medicine, gynecology, pediatrics, and orthopedics, to fine-tune the model with a domain-specific million-level instruct data.
Continuously iterate and update. Subsequent releases will include Li Shizhen, Wang Shuhe, Huangfu Mi, Sun Simiao, Ge Hong, and Qihuang version of the large language model for Traditional Chinese Medicine.
Explore efficient domain fine-tuning strategies.
## 待做清单
1.采用多任务诊疗分解策略，基于内外妇儿骨等多学科数据构建领域百万级instruct数据微调模型
2.持续迭代更新，后续将发布李时珍、王叔和、皇甫谧、孙思邈、葛洪、岐黄版中医药大语言模型
3.探索高效领域微调策略
## Acknowledgements
The Lora fine-tuning part of this project draws on the ideas of alpaca-lora and Chinese-Vicuna. We would like to express our gratitude to the members of the relevant research teams.
This research is for academic research use only, commercial use is not allowed without permission, and it is not to be used in medical scenarios or scenarios with potential medical intent for clinical practice. This large language model for Traditional Chinese Medicine is still in the laboratory testing stage. The emerging syndrome classification and prescription generation capabilities at this stage are still rudimentary, and it does not yet have a highly reliable clinical diagnostic and therapeutic capability for gynecology and other clinical specialties. The output results are for internal reference testing only. Real medical diagnosis and decision-making still need to be issued by experienced physicians through a strictly regulated diagnostic and therapeutic process.
Data processing and annotation is one of the important steps in training the model. We sincerely welcome Traditional Chinese Medicine practitioners with strong TCM thinking and innovative spirit to join us. We will also declare corresponding contributions at the data level. We look forward to the day when we can achieve a reliable general-purpose AI for Traditional Chinese Medicine, allowing the ancient Chinese medicine to blend with modern technology and shine anew. This is also the ultimate mission of this project. If interested, please send an email to 21110860035@m.fudan.edu.cn.
This project is jointly guided by Professor Zhang Wenqiang from Fudan University and Professor Wang Haofen from Tongji University. It is completed by Kang Yanlan, Chang Yang, and Fu Jiyuan, members of the ROI Lab at Fudan University.
