Personalization and Adaptation Methods for Large Language Models: A Survey

Abstract

Large Language Models (LLMs) have significantly reshaped natural language processing by demonstrating strong capabilities in modeling long-range dependencies, capturing contextual semantics, and generating coherent natural language across a wide range of tasks. However, their performance often degrades when applied to specialized or low-resource domains, primarily due to domain shift and the presence of domainspecific terminology and knowledge. In addition, adapting LLMs to evolving domain requirements often incurs substantial computational costs. These limitations raise challenges in terms of reliability and contextual fidelity in real-world applications. This study presents a comprehensive overview of domain adaptation of Large Language Models and examines existing methods, with the objective of improving their accuracy, robustness, and effectiveness in specialized application settings.