TCMD: A Traditional Chinese Medicine QA Dataset for Evaluating Large Language Models

TL;DR

This paper introduces TCMD, a comprehensive Chinese Medicine QA dataset designed to evaluate and improve large language models' performance in Traditional Chinese Medicine question-answering tasks.

Contribution

The paper presents a new large-scale TCM QA dataset with annotated questions, supporting comprehensive assessment and analysis of LLMs in the TCM domain.

Findings

Current LLMs show inconsistency in TCM QA tasks.

Evaluation reveals gaps in LLMs' robustness and accuracy.

The dataset facilitates future development of TCM-specific LLMs.

Abstract

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.