GPT versus ERNIE for National Traditional Chinese Medicine Licensing Examination: Does cultural background matter?
Purpose: This study evaluates the performance of large language models (LLMs) in the context of the Chinese National Traditional Chinese Medicine Licensing Examination (TCMLE). - Materials and Methods: We compared the performances of different versions of Generative Pre-trained Transformer (GPT) and...
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| Hauptverfasser: | , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2 July 2025
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| In: |
Journal of integrative and complementary medicine
Year: 2025, Pages: ? |
| ISSN: | 2768-3613 |
| DOI: | 10.1089/jicm.2024.0902 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1089/jicm.2024.0902 Verlag, lizenzpflichtig, Volltext: http://www.liebertpub.com/doi/10.1089/jicm.2024.0902 
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| Verfasserangaben: | Erfan Ghanad, Christel Weiß, Hui Gao, Christoph Reißfelder, Kamal Hummedah, Lei Han, Leihui Tong, Chengpeng Li, and Cui Yang |
| Zusammenfassung: | Purpose: This study evaluates the performance of large language models (LLMs) in the context of the Chinese National Traditional Chinese Medicine Licensing Examination (TCMLE). - Materials and Methods: We compared the performances of different versions of Generative Pre-trained Transformer (GPT) and Enhanced Representation through Knowledge Integration (ERNIE) using historical TCMLE questions. - Results: ERNIE-4.0 outperformed all other models with an accuracy of 81.7%, followed by ERNIE-3.5 (75.2%), GPT-4o (74.8%), and GPT-4 turbo (50.7%). For questions related to Western internal medicine, all models showed high accuracy above 86.7%. - Conclusion: The study highlights the significance of cultural context in training data, influencing the performance of LLMs in specific medical examinations. |
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| Beschreibung: | Gesehen am 02.09.2025 |
| Beschreibung: | Online Resource |
| ISSN: | 2768-3613 |
| DOI: | 10.1089/jicm.2024.0902 |