Personalized Large Language Models
It addresses the limitation of universal LLMs in scenarios requiring personalized responses, such as recommendation systems and chatbots, but is incremental as it compares existing methods like fine-tuning and zero-shot reasoning.
This paper tackles the problem of personalizing large language models (LLMs) for subjective tasks like emotion recognition and hate speech detection, showing that personalized fine-tuning improves model reasoning compared to non-personalized models with consistent performance gains across different LLM architectures.
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.