Cross-Cultural Adaptation Framework for Enhancing Large Language Model Outputs in Multilingual Contexts

Abstract

This paper presents a comprehensive Cross-Cultural Adaptation Framework for enhancing Large Language Model (LLM) outputs in multilingual contexts. While LLMs demonstrate impressive capabilities in generating language-specific content, their cross-cultural adaptability remains limited, creating challenges in global deployment scenarios. The proposed framework addresses these limitations through a modular architecture integrating cultural context detection, knowledge integration, adaptive response generation, and cultural evaluation components. Experimental evaluation across diverse cultural contexts (East Asian, Western European, Middle Eastern, South Asian, and Latin American) and multiple application domains demonstrates significant improvements in cultural appropriateness (91% ± 0.04) compared to baseline approaches (62-83%). Performance analysis reveals consistent adaptation quality across linguistic pairs while highlighting domain-specific variations. The framework achieves these improvements with moderate computational overhead (15-20%), making it viable for most production environments. Ablation studies confirm the contribution of each component to overall performance, with the cultural context detector providing the most substantial impact (25.3% performance reduction when removed). This research advances the state-of-the-art in multilingual LLM deployment by providing a systematic approach to cultural adaptation that extends beyond mere translation, enabling more appropriate, effective, and culturally sensitive language generation across global contexts.