文献类型：期刊文献

英文题名：How adaptable is the ChatGPT large language model for translating different text types? An Empirical Study

作者：Qiu, Xinwei[1]; Jia, Hui[1]

机构：[1] East China University of Science and Technology, School of Foreign Languages, China

年份：2025

起止页码：13

外文期刊名：Proceedings - 2025 4th International Conference on Computer Technologies, ICCTech 2025

收录：EI(收录号：20253218951415)

语种：英文

外文关键词：Artificial intelligence - Computer aided language translation - Control theory - Machine translation - Natural language processing systems - Text processing

摘要：Recent advancements in Artificial Intelligence and Large Language Models, such as ChatGPT, have improved machine translation capabilities. However, little research explores their adaptability across different text types. This study evaluates ChatGPT's translation quality using Reiss' text typology theory, focusing on three text types: informative, expressive, and operative. Through a corpus-based approach, the study combines automated and human evaluations, with textual features analyzed using Coh-Metrix 3.0. The results show significant variation in ChatGPT's translation quality, with the best performance in informative texts and lower quality in expressive and operative ones. The study also shows that while ChatGPT captures general variations in text types, it struggles to replicate the nuanced characteristics of specific genres as accurately as human translators. ? 2025 IEEE.