XuanHuGPT: parameter-efficient fine-tuning of large language model in the field of traditional Chinese medicine
Xuming Tong, Xiaozheng Ding, Huiru Jia, Yanhong Yuan, Liyan Liu, Yapeng Wang, Zhang Xiong, Xu Yang, Sio Kei Im, Mini Han Wang · 2025
Read XuanHuGPT: parameter-efficient fine-tuning of large language model in the field of traditional Chinese medicine by Xuming Tong, Xiaozheng Ding, Huiru Jia, Yanhong Yuan, Liyan Liu, Yapeng Wang, Zhang Xiong, Xu Yang, Sio Kei Im, Mini Han Wang (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12648848.txt.
Description
Large Language Models (LLMs) have demonstrated exceptional generalization capabilities across various fields, including their application in Traditional Chinese Medicine (TCM). However, the performance of existing LLMs in TCM-specific tasks remains limited due to the lack of optimization for TCM knowledge during the pre-training phase, insufficient datasets, and the constraints of fine-tuning techniques.
Catalog record
| CREATOR | Xuming Tong, Xiaozheng Ding, Huiru Jia, Yanhong Yuan, Liyan Liu, Yapeng Wang, Zhang Xiong, Xu Yang, Sio Kei Im, Mini Han Wang |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 73 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |