Cross-Cultural Implications of Large Language Models: An Extended Comparative Analysis

Xinyang Shan, Yuanyuan Xu, Yining Wang, Yin-Shan Lin, Yunshi Bao

Abstract: This article examines the impacts of deploying large language models (LLMs) across diverse cultural contexts, emphasizing the challenges and opportunities related to their linguistic adaptability and cultural sensitivity. As globalization progresses, the necessity for LLMs to operate effectively and sensitively in multilingual and multicultural environments becomes increasingly critical. This study conducts a comprehensive multilingual analysis to explore how these models navigate linguistic nuances and cultural idiosyncrasies when generating and interpreting text. By investigating a diverse array of languages and cultural settings, the research identifies crucial challenges that current models face, such as biases and inaccuracies in languages with less digital representation. These biases not only affect the accuracy of the models but also potentially exacerbate existing social inequalities, particularly in marginalized communities. To address these challenges, this article proposes innovative strategies to enhance the cultural and linguistic effectiveness of LLMs. Firstly, it emphasizes the importance of incorporating culturally inclusive training datasets during the development phases of AI systems to ensure that the models are exposed to a diverse range of languages and cultural contexts. Secondly, it suggests integrating cultural experts into development teams to provide valuable insights into linguistic peculiarities and cultural nuances, thereby improving the models’ accuracy and sensitivity. Through quantitative and qualitative methods, the study assesses the performance of LLMs across various metrics, including cultural sensitivity and user satisfaction. The quantitative analysis involves using a series of culturally specific prompts to measure the accuracy of language generation and comprehension, while the qualitative evaluation involves detailed feedback from language experts and native speakers to assess the contextual appropriateness and cultural relevance of the generated texts. The findings reveal that while LLMs perform excellently in handling resource-rich languages, there remains a significant gap in their ability to manage languages with fewer resources.