TCMD: A Traditional Chinese Medicine QA Dataset for Evaluating Large Language Models
This provides a domain-specific benchmark for evaluating LLMs in Traditional Chinese Medicine, addressing a gap due to limited medical datasets, but it is incremental as it focuses on a new dataset rather than novel methods.
The authors introduced TCMD, a Traditional Chinese Medicine question-answering dataset to evaluate large language models, finding that current models show inconsistencies and shortcomings in solving TCM tasks.
The recently unprecedented advancements in Large Language Models (LLMs) have propelled the medical community by establishing advanced medical-domain models. However, due to the limited collection of medical datasets, there are only a few comprehensive benchmarks available to gauge progress in this area. In this paper, we introduce a new medical question-answering (QA) dataset that contains massive manual instruction for solving Traditional Chinese Medicine examination tasks, called TCMD. Specifically, our TCMD collects massive questions across diverse domains with their annotated medical subjects and thus supports us in comprehensively assessing the capability of LLMs in the TCM domain. Extensive evaluation of various general LLMs and medical-domain-specific LLMs is conducted. Moreover, we also analyze the robustness of current LLMs in solving TCM QA tasks by introducing randomness. The inconsistency of the experimental results also reveals the shortcomings of current LLMs in solving QA tasks. We also expect that our dataset can further facilitate the development of LLMs in the TCM area.