Personalized Large Language Models

2024/02/14 by Stanisław Woźniak, Bartłomiej Koptyra, Woźniak, Stanisław +7 · 9 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2402.09269

openalex publication_date 2024/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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• When Personalization Tricks Detectors: The Feature-Inversion Trap in Machine-Generated Text Detection
• GRAVITY: A Framework for Personalized Text Generation via Profile-Grounded Synthetic Preferences
• EMNLP: Educator-role Moral and Normative Large Language Models Profiling
• Accessibility Scout: Personalized Accessibility Scans of Built Environments
• Backtranslation and paraphrasing in the LLM era? Comparing data augmentation methods for emotion classification
• CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering
• Personalised Explanations in Long-term Human-Robot Interactions

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