JMIR Publications

ABSTRACT

Background:

Research on large language models (LLMs) in the medical field has predominantly focused on models trained with English-language corpora, evaluating their performance within English-speaking contexts. The performances of models trained with non-English language corpora and their performance in non-English contexts remain underexplored.

Objective:

We used the Chinese Medical Examination (CNMLE) as a benchmark and constructed analogous questions to evaluate the performances of LLMs trained on different languages corpora.

Methods:

Under different prompt settings, we sequentially posed questions to seven LLMs: two primarily trained on English-language corpora and five primarily on Chinese-language corpora. The models' responses were compared against standard answers to calculate the accuracy rate of each model. Further subgroup analyses were conducted by categorizing the questions based on various criteria. We also collected error sets to explore patterns of mistakes across different models.

Results:

Under zero-shot setting, six out of seven models exceeded the passing level, with the highest accuracy rate achieved by the Chinese LLM Baichuan (86.67%), followed by ChatGPT (83.83%). In the constructed questions, all seven models exceeded the passing threshold, with Baichuan maintaining the highest accuracy rate (87.00%). In few-shot learning, all models exceeded the passing threshold. Baichuan, ChatGLM, and ChatGPT retained the highest accuracy. While Llama showed marked improvement over prior tests, the relative performance rankings of other models stayed similar to previous results. In subgroup analyses, English models demonstrated comparable or superior performance to Chinese models on questions related to ethics and policy. All models except Llama generally had higher accuracy rates for simple questions compared to complex ones. The error set of ChatGPT was similar to those of other Chinese models. Multi-model cross verification outperformed single model, particularly improving accuracy rate on simple questions. The implementation of dual-model and tri-model verification achieved accuracy rates of 94.17% and 96.33% respectively.

Conclusions:

At the current level, LLMs trained primarily on English corpora and those trained mainly on Chinese corpora perform similarly well in CNMLE, with Chinese models still outperforming. The performance difference between ChatGPT and other Chinese LLMs are not solely due to communication barriers but are more likely influenced by disparities in the training data. By employing a method of cross-verification with multiple LLMs, excellent performance can be achieved in medical examinations.