A Survey on Personalized A lignment— T he Missing Piece for Large Language Models in Real-World Applications (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.

## introduction large language models (llms) have revolutionized natural language processing tasks @xcite @xcite , achieved by aligning their behaviors with human preferences @xcite . while current alignment techniques optimize for universal human values such as helpfulness, honesty, and harmlessness @xcite , their transition to real-world applications reveals a crucial limitation: the inability to adapt to diverse user needs @xcite , leading to reduced satisfaction and systematic bias against minority groups @xcite , as exemplified in personalization has driven substantial socioeconomic value in traditional artificial intelligence (ai) systems like search engines @xcite , advertising @xcite , and recommendation @xcite . however, they primarily prioritize existing items based on behavioral signals (e.g., clicks, purchases). in contrast, llm personalization must interpret a broader spectrum of preference indicators such as dialogue history @xcite and generate responses through sophisticated reasoning. to address the challenges, we propose a comprehensive personalized alignment framework with three components: (1) preference memory management for maintaining user-specific knowledge, (2) personalized generation and rewarding for incorporating personalized patterns, and (3) alignment through feedback for improving preference memory and generations. as illustrated in figure 1 , we establish a hierarchy of alignment objectives where universal values define ethical boundaries within ## formalizing personalized alignment figure 2 illustrates our framework for personalized alignment ( §3.1), guided by hierarchical alignment objectives ( §3.2). ## techniques for personalized alignment this section examines three core components in the personalized alignment framework. figure 3 illustrates the taxonomy of this section. ## evaluation of personalized alignment evaluating personalized alignment presents unique challenges beyond traditional llm evaluation, requiring simultaneous assessment of universal value alignment and individual preference satisfaction. this section examines current evaluation approaches and their limitations. ## application of personalized alignment personalized llms extend beyond chatbots to transform productivity, daily experience, and social welfare through user-adaptive interactions. we discuss the potential application of personalized alignment in appendix c. ## risks of personalized alignment personalized alignment introduces interconnected risks at both individual and societal levels @xcite . individual risks include: (1) portrait abuse through targeted attacks @xcite @xcite , such as personalized phishing based on user preferences; mitigation requires both technical solutions (federated learning, differential privacy, debiasing techniques) and operational measures (bias auditing, transparent policies, user control mechanisms). however, balancing these safeguards with effective personalization remains challenging. ## key challenges and future directions current challenges in personalized alignment span three aspects: foundational methodologies, technical implementation, and practical deployment. foundational challenges focus on understanding and modeling user preferences: (1) capturing complex personalized preferences that evolve temporally @xcite , are influenced by social context @xcite , vary across scenarios (e.g., work vs. life situations) @xcite , and exhibit response-dependent trade-offs between different dimensions (e.g., quality vs. speed) @xcite ; technical challenges concern the implementation of personalized systems: (1) integrating multimodal signals (e.g., text, vision, audio) to better understand and generate personalized content @xcite ; (2) incorporating user preferences into long-chain reasoning while preserving interpretability and contextual consistency @xcite ; (3) balancing performance and computational efficiency for realtime personalization; (4) ensuring personalized models can effectively generalize from simple to complex tasks with weak supervisions @xcite . practical challenges address deployment con-cerns while prioritizing universal values: (1) protecting user privacy and security while maintaining personalization effectiveness; (2) ensuring fairness across different user groups, system transparency, and positive societal impact; (3) coordinating multiple personalized agents while preventing harmful emergent behaviors; (4) safely removing dangerous capabilities while preserving personalization performance and ensuring system accessibility. ## conclusion we present a comprehensive examination of personalized alignment in llms, demonstrating its critical role in bridging universal value alignment with individual user needs. our unified framework, that encompasses preference memory management, personalized generation and rewarding, and alignment through feedback, provides a structured approach for advancing this field. by analyzing various implementation strategies and diverse applications, we reveal the significant potential and current limitations of personalized alignment. looking forward, success in this field requires continued innovation in technical approaches, evaluation frameworks, and ethical considerations, especially as llms become increasingly prevalent in serving diverse users.