Conference Paper Published

Abstract

Large language models (LLMs) have demonstrated increasing proficiency in general-purpose translation, yet their effectiveness in creative domains such as game localization remains underexplored. This study focuses on the role of LLMs in game localization from both linguistic quality and sociocultural adequacy
through a case study of the video game Black Myth: Wukong.

Results indicate that LLMs demonstrate adequate competence in accuracy and fluency, achieving performance comparable to human translators. However, limitations remain in the literal translation of culture-specific terms and
offensive language. Human oversight is required to ensure nuanced cultural authenticity and sensitivity. Insights from human evaluations also suggest that current automatic metrics and the Multidimensional Quality Metrics framework may be inadequate for evaluating creative translation. Finally, varying human
preferences in localization pose a learning ambiguity for LLMs to perform optimal translation strategies. The findings highlight the potential and shortcomings of LLMs to serve as collaborative tools in game localization workflows. Data are available at https://github.com/zcocozz/wukong-localization.