计算机科学
语言模型
领域(数学)
自然语言处理
中文
数据建模
人工智能
语言学
软件工程
数学
哲学
纯数学
作者
Jinyang Zhu,Qing-Yue Gong,Chunfang Zhou,Huidan Luan
标识
DOI:10.1145/3644116.3644294
摘要
The success of ChatGPT has showcased the potential applications of Large Language Models (LLMs) in the field of Traditional Chinese Medicine (TCM), encompassing areas such as medical diagnosis, adjunctive therapy, and TCM talent cultivation. However, the current challenges, including hardware constraints, insufficient model domain knowledge, and difficulties in domain-specific evaluation, have constrained the fusion of LLMs with TCM. In an attempt to address these issues, this paper introduces ZhongJing, a domain-specific LLM fine-tuned within the domain of TCM, capable of generating responses at a rate of 8 tokens per second, smoothly operating on local personal computers. To assess the model's domain expertise, this paper introduces the TCMEval evaluation method, designed concerning medical students' exams. Experimental results demonstrate that ZhongJing achieves a 6.49 TCMEval Score improvement over Chinese-LLaMA2 in the field of TCM, indicating the model's ability to generate more specialized responses compared to baseline models.
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