A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications
Journal:
arXiv
Published Date:
Mar 21, 2025
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.
