Tianyi: A Traditional Chinese Medicine all-rounder language model and its Real-World Clinical Practice
Journal:
arXiv
Published Date:
May 19, 2025
Abstract
Natural medicines, particularly Traditional Chinese Medicine (TCM), are
gaining global recognition for their therapeutic potential in addressing human
symptoms and diseases. TCM, with its systematic theories and extensive
practical experience, provides abundant resources for healthcare. However, the
effective application of TCM requires precise syndrome diagnosis, determination
of treatment principles, and prescription formulation, which demand decades of
clinical expertise. Despite advancements in TCM-based decision systems, machine
learning, and deep learning research, limitations in data and single-objective
constraints hinder their practical application. In recent years, large language
models (LLMs) have demonstrated potential in complex tasks, but lack
specialization in TCM and face significant challenges, such as too big model
scale to deploy and issues with hallucination. To address these challenges, we
introduce Tianyi with 7.6-billion-parameter LLM, a model scale proper and
specifically designed for TCM, pre-trained and fine-tuned on diverse TCM
corpora, including classical texts, expert treatises, clinical records, and
knowledge graphs. Tianyi is designed to assimilate interconnected and
systematic TCM knowledge through a progressive learning manner. Additionally,
we establish TCMEval, a comprehensive evaluation benchmark, to assess LLMs in
TCM examinations, clinical tasks, domain-specific question-answering, and
real-world trials. The extensive evaluations demonstrate the significant
potential of Tianyi as an AI assistant in TCM clinical practice and research,
bridging the gap between TCM knowledge and practical application.
