Qibo: A Large Language Model for Traditional Chinese Medicine
Heyi Zhang Xin Wang Zhaopeng Meng Yongzhe Jia Dawei Xu
In the field of Artificial Intelligence, Large Language Models (LLMs) have
demonstrated significant advances in user intent understanding and response in
a number of specialized domains, including medicine, law, and finance. However,
in the unique domain of traditional Chinese medicine (TCM), the performance
enhancement of LLMs is challenged by the essential differences between its
theories and modern medicine, as well as the lack of specialized corpus
resources. In this paper, we aim to construct and organize a professional
corpus in the field of TCM, to endow the large model with professional
knowledge that is characteristic of TCM theory, and to successfully develop the
Qibo model based on LLaMA, which is the first LLM in the field of TCM to
undergo a complete training process from pre-training to Supervised Fine-Tuning
(SFT). Furthermore, we develop the Qibo-benchmark, a specialized tool for
evaluating the performance of LLMs, which is a specialized tool for evaluating
the performance of LLMs in the TCM domain. This tool will provide an important
basis for quantifying and comparing the understanding and application
capabilities of different models in the field of traditional Chinese medicine,
and provide guidance for future research directions and practical applications
of intelligent assistants for traditional Chinese medicine. Finally, we
conducted sufficient experiments to prove that Qibo has good performance in the
field of traditional Chinese medicine.