Fine-Tuning Large Language Models on Cultural Nuances for Linguistically Driven Hyper-Personalization: A Literature Review

Abstract

As large language models (LLMs) rapidly integrate into business and commerce environments, the ability to accommodate cultural nuances and linguistic variations has become increasingly critical. Hyper-personalization—tailoring system outputs to individual consumers and cultural contexts—can enhance customer trust, engagement, and effectiveness in areas such as marketing, customer service, and product recommendations. This literature review synthesizes studies published through early 2024 that consider the fine-tuning of LLMs to reflect cultural and linguistic attributes. We assess theoretical frameworks for cultural adaptation, approaches to data curation and representation, methods for language model fine-tuning, and the implications of these techniques for business and commerce. Additionally, we address ethical, fairness, and bias considerations, as well as the challenges and future directions in this emerging field. The evidence suggests that culturally nuanced fine-tuning can unveil unseen levels of hyper-personalization in business applications, though continued research is still warranted to handle data scarcity, examine cultural appropriateness, and alleviate risks of stereotyping and bias.