Exploring the Comprehension of ChatGPT in Traditional Chinese Medicine Knowledge
CoRR(2024)
摘要
No previous work has studied the performance of Large Language Models (LLMs)
in the context of Traditional Chinese Medicine (TCM), an essential and distinct
branch of medical knowledge with a rich history. To bridge this gap, we present
a TCM question dataset named TCM-QA, which comprises three question types:
single choice, multiple choice, and true or false, to examine the LLM's
capacity for knowledge recall and comprehensive reasoning within the TCM
domain. In our study, we evaluate two settings of the LLM, zero-shot and
few-shot settings, while concurrently discussing the differences between
English and Chinese prompts. Our results indicate that ChatGPT performs best in
true or false questions, achieving the highest precision of 0.688 while scoring
the lowest precision is 0.241 in multiple-choice questions. Furthermore, we
observed that Chinese prompts outperformed English prompts in our evaluations.
Additionally, we assess the quality of explanations generated by ChatGPT and
their potential contribution to TCM knowledge comprehension. This paper offers
valuable insights into the applicability of LLMs in specialized domains and
paves the way for future research in leveraging these powerful models to
advance TCM.
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