A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications
This survey tackles the problem of making LLMs more adaptable and ethically aligned for real-world applications, but it is incremental as it synthesizes existing work rather than introducing new methods.
The paper addresses the limitation of large language models in adapting to individual preferences while maintaining universal human values, proposing personalized alignment as a solution and providing a comprehensive survey of current techniques, risks, and challenges.
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.