Cultural Knowledge Conservation and Control in Large Language Models

Large language models (LLMs) possess cultural knowledge that is not always apparent in multilingual interactions. This research reveals an "explicit–implicit localization gap," where LLMs perform better on culturally specific tasks when explicitly prompted with cultural context compared to when only the language of the prompt suggests the culture. The study demonstrates that providing explicit cultural cues enhances localization but can reduce response diversity and increase stereotypes. Conversely, the researchers identified "cultural customization vectors" within the models that, when applied, can steer responses toward specific cultures while maintaining diversity and reducing stereotypical outputs. Ultimately, the work suggests that culturally aware AI systems require more than just multilingual capabilities, necessitating methods to actively engage the latent cultural understanding within these models.