The Use of Large Language Models for Translating Buddhist Texts from Classical Chinese to Modern English: An Analysis and Evaluation with ChatGPT 4, ERNIE Bot 4, and Gemini Advanced
This study conducts a comprehensive evaluation of large language models (LLMs), including ChatGPT 4, ERNIE Bot 4, and Gemini Advanced, in the context of translating Buddhist texts from classical Chinese to modern English. Focusing on three distinct Buddhist texts encompassing various literary forms...
| Autore principale: | |
|---|---|
| Tipo di documento: | Elettronico Articolo |
| Lingua: | Inglese |
| Verificare la disponibilità: | HBZ Gateway |
| Interlibrary Loan: | Interlibrary Loan for the Fachinformationsdienste (Specialized Information Services in Germany) |
| Pubblicazione: |
2024
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| In: |
Religions
Anno: 2024, Volume: 15, Fascicolo: 12 |
| Altre parole chiave: | B
Buddhist texts
B large language models (LLMs) B translation evaluation |
| Accesso online: |
Volltext (kostenfrei) Volltext (kostenfrei) |
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| 520 | |a This study conducts a comprehensive evaluation of large language models (LLMs), including ChatGPT 4, ERNIE Bot 4, and Gemini Advanced, in the context of translating Buddhist texts from classical Chinese to modern English. Focusing on three distinct Buddhist texts encompassing various literary forms and complexities, the analysis examines the models’ capabilities in handling specialized Buddhist terminology, classical Chinese grammar, and the translation of complex, lengthy sentences. The study employs a methodology where selected excerpts from these texts are translated by the LLMs, followed by an in-depth analysis comparing these machine-generated translations to human translations. The evaluation criteria include word translation accuracy, the ability to recognize and correctly interpret specific meanings within both classical and modern contexts, and the completeness of phrases without omitting or unnecessarily adding words. The findings reveal significant variations in the performance of these LLMs, with detailed observations on their strengths and weaknesses in translating specialized terms, managing grammatical structures unique to classical Chinese, and maintaining the integrity of the original texts’ meanings. This paper aims to shed light on the potential and limitations of using LLMs for translating complex literary works from ancient to modern languages, contributing valuable insights into the field of computational linguistics and the ongoing development of translation technologies. | ||
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