Performance of Large Language Models in the Non-English Context: Qualitative Study of Models Trained on Different Languages in Chinese Medical Examinations
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 using a method of cross-verification with multiple LLMs, excellent performance can be achieved in medical examinations.
Authors
- Dongfang Sheng (ORCID: https://orcid.org/0000-0003-1720-4993)
- Lirui Duan
- Shuo Xu (ORCID: https://orcid.org/0000-0001-5310-9673)
- Zhong Yao (ORCID: https://orcid.org/0000-0001-7148-8275)
- Lingyi Chi (ORCID: https://orcid.org/0000-0002-1727-0125)
Institutions
- Shandong University (CN)
- Shandong Management University (CN)
- Qilu Hospital of Shandong University (CN)
- Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-06-27
- DOI
- https://doi.org/10.2196/69485
- Citations
- 9
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 5.89