Referential explicitation in translations by large language models, neural machine translation system, and human translators across genres

Abstract This study examines referential explicitation in Large Language Models (LLMs)-based machine translation (MT) compared to neural machine translation (NMT) and human translations. Referential explicitation—the process of making implicit referential expressions explicit in the target text, has been recognized as a crucial aspect of translation studies. Through Multidimensional Analysis (MDA), findings indicate that all translation modes perform referential explicitation across various genres including news, academic, and fiction. The degree of explicitation in LLM-based machine translation falls between human translations and neural machine translations. Moreover, machine translations prioritize clarity and transparency, whereas human translations tend to preserve implicit references especially in fiction. This difference underscores the challenge for neural machine translations in balancing clarity with cultural nuances. Notably, LLM-based machine translations demonstrate improvements, achieving performance closer to human translators due to their distinct underlying logic compared to neural machine translation. Additionally, genre-specific patterns reveal consistently high levels of explicitation in news and academic texts, while fiction translations vary in their preservation of implicit references. This study enhances the theoretical understanding of translation universals and contributes to the development of advanced evaluation metrics for machine translation.