A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications

2025-03-21

AI Summary

Current LLM alignment methods struggle to simultaneously accommodate individual preferences and universal values, suffering from a “one-size-fits-all” limitation. This paper proposes the first systematic framework for personalized alignment, comprising three core components: preference memory management, adaptive generation, and feedback-driven alignment. It introduces the first theoretical taxonomy that rigorously distinguishes personalized alignment from general alignment, clarifying its fundamental differences and ethical boundaries in modeling. The framework integrates preference modeling, incremental fine-tuning, RLHF/RLAIF, memory-augmented generation, and multi-dimensional evaluation. We present the first comprehensive technology landscape of personalized alignment, identifying critical risks—including preference overfitting, value drift, and memory leakage—as well as key technical bottlenecks. Our work establishes a methodological foundation and practical guidelines for developing next-generation LLM applications that are trustworthy, customizable, and ethically grounded.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to individual preferences while maintaining alignment with universal human values. Current alignment techniques adopt a one-size-fits-all approach that fails to accommodate users' diverse backgrounds and needs. This paper presents the first comprehensive survey of personalized alignment-a paradigm that enables LLMs to adapt their behavior within ethical boundaries based on individual preferences. We propose a unified framework comprising preference memory management, personalized generation, and feedback-based alignment, systematically analyzing implementation approaches and evaluating their effectiveness across various scenarios. By examining current techniques, potential risks, and future challenges, this survey provides a structured foundation for developing more adaptable and ethically-aligned LLMs.

Problem

• Adapting LLMs to individual preferences within ethical boundaries
• Overcoming one-size-fits-all alignment for diverse user needs
• Developing frameworks for personalized and ethically-aligned LLMs

Innovation

• Personalized alignment for LLMs adaptation
• Preference memory management framework
• Feedback-based alignment techniques