LLM Personalization

LLM personalization aims to tailor large language models (LLMs) to individual user preferences, generating outputs aligned with specific styles, needs, and knowledge bases. Current research focuses on efficient methods for incorporating user data, including techniques like parameter-efficient fine-tuning (PEFT), prompt engineering with user profiles and historical data, and knowledge graph integration to personalize factual knowledge without extensive model retraining. This field is significant because it enhances user experience, improves the utility of LLMs across diverse applications, and addresses challenges related to model copyright protection and user privacy.