  Large language models (LLMs) have significantly advanced Natural Language
Processing (NLP) tasks in recent years. However, their universal nature poses
limitations in scenarios requiring personalized responses, such as
recommendation systems and chatbots. This paper investigates methods to
personalize LLMs, comparing fine-tuning and zero-shot reasoning approaches on
subjective tasks. Results demonstrate that personalized fine-tuning improves
model reasoning compared to non-personalized models. Experiments on datasets
for emotion recognition and hate speech detection show consistent performance
gains with personalized methods across different LLM architectures. These
findings underscore the importance of personalization for enhancing LLM
capabilities in subjective text perception tasks.
