Abstract
In the context of the accelerated globalization process, cross-language communication is becoming increasingly frequent, involving a wider variety of languages and cultural differences. This has driven a rapid growth in the demand for multilingual translation. This trend has led to the widespread application and in-depth research of large language models based on artificial intelligence in the field of translation. However, the diversity and complexity of languages present numerous challenges for these language models during their development, especially when it comes to accurately addressing cultural differences between source and target languages in translation practice. Specifically, AI translation tools often struggle to fully convey the cultural connotations and values of the source language in English translations, and may even fail to adequately address or downplay cultural differences. This can result in mistranslations or hollow translations at the cultural level.
Published Version
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