LLM-Enhanced Cultural Sensitivity Detection in Games Localization: A Comparative Framework for Multimedia Content

This research explores the innovative application of Large Language Models (LLMs) in quality assessment of games and multimedia content localization, with a specific focus on the effectiveness of cultural sensitivity detection. Based on the author's localization production experience at Blizzard Entertainment, the study designs a comparative evaluation framework to analyze the differences between LLM-assisted and traditional human quality assessment methods in identifying culture-specific elements, idioms, and emotional connotations. Through case studies, the research compares the performance of human evaluation, existing automation tools, and LLM-assisted assessment in games, video, and marketing content, particularly emphasizing their application value in non-standard language outsourcing pipelines. The methodology integrates data analysis techniques with localization quality management principles, leveraging the researcher's expertise in translation technology and Tableau business intelligence analysis tools to develop evaluation metrics that quantify LLM effectiveness in cross-cultural communication. By analyzing LLM capabilities in identifying cultural nuances, this study aims to provide practical quality management tools for international digital content producers, thereby enhancing the effectiveness of global content strategies. This research holds significant implications for enhancing global competitiveness in the digital entertainment market. As gaming and digital media companies expand their international influence, precise cross-cultural content adaptation has become a critical competitive factor. The research outcomes will help enterprises more effectively communicate cultural values, enhance leadership in the global digital content market, and provide technical support for international cooperation and cultural exchange through digital content. Simultaneously, the innovative localization quality assessment framework will strengthen America's technological advantages in AI applications and langue.