Awesome-Personalized-Alignment

👤 Curator

Yongqi Li — Homepage

📢 News

• [2026/08/06] A note from the repo maintainer: with the arrival of the agent era, personalized alignment has started to intersect with memory and self-improvement as well. We are therefore working on re-taxonomizing existing work. Stay tuned.🤗
• [2026/04/10] 🚀 We have launched our website! Check out the curated collection of personalized alignment papers here: Awesome Personalized Alignment.

Scope

Personalized Alignment can be broadly categorized into two directions:

1. Personalized Assistant: Building AI assistants that adaptively serve users' individual needs, preferences, and goals.
2. Intrinsic Personalization: Equipping AI with internalized personality traits and characteristics, commonly seen in role-playing scenarios, social simulations, and agent-based modeling.

Note: This repository focuses primarily on Personalized Assistant. For work on Intrinsic Personalization, we recommend exploring the literature on role-playing agents.

Papers

Note: All papers listed here are manually curated by the maintainers through daily reading to ensure high quality and relevance.

Position and Survey Papers

• [2025/04] A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models
• [2025/03] A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications
• [2024/12] Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization
• [2024/12] Personalized Multimodal Large Language Models: A Survey
• [2024/11] Personalization of Large Language Models: A Survey
• [2024/10] When large language models meet personalization: perspectives of challenges and opportunities
• [2024/07] The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm
• [2024/05] Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations
• [2024/04] The benefits, risks and bounds of personalizing the alignment of large language models to individuals
• [2024/02] Position: A Roadmap to Pluralistic Alignment

Personalized Preference

• [2026/09] PGMem: Tightly Coupled Persona–Memory Graph for Lifelong Personalized Agents
• [2026/08] PAST-Bench: Benchmarking the Foundations of Recursive Self-Improvement in Personal Agents
• [2026/08] The Personalization Mirage: How LLMs Fabricate User Profiles, and Why Self-Monitoring Misleads
• [2026/06] TriAlign: Towards Universal Truth Consistency in Personalized LLM Alignment
• [2026/06] Beyond Isolated Behaviors: Hierarchical User Modeling for LLM Personalization
• [2026/06] From Empathy to Personalized Empathy: Adapting Empathetic Strategies to Individual Users
• [2026/06] Preference-Aware Rubric Learning for Personalized Evaluation
• [2026/05] MATO: Multi-objective Personalized Alignment with Test-time Optimization for Large Language Models
• [2026/05] Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation
• [2026/05] From Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning
• [2026/05] APM: Evaluating Style Personalization in LLMs with Arbitrary Preference Mappings
• [2026/05] From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG
• [2026/05] Test-Time Personalization: A Diagnostic Framework and Probabilistic Fix for Scaling Failures
• [2026/04] Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization
• [2026/04] From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents
• [2026/04] Personalized Benchmarking: Evaluating LLMs by Individual Preferences
• [2026/04] CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors
• [2026/04] PersonaVLM: Long-Term Personalized Multimodal LLMs
• [2026/04] Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization
• [2026/03] AlpsBench: An LLM Personalization Benchmark for Real-Dialogue Memorization and Preference Alignment
• [2026/03] Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment
• [2026/03] Language Models Don't Know What You Want: Evaluating Personalization in Deep Research Needs Real Users
• [2026/02] Learning to summarize user information for personalized reinforcement learning from human feedback
• [2026/02] Think-While-Generating: On-the-Fly Reasoning for Personalized Long-Form Generation
• [2026/02] Swap-guided Preference Learning for Personalized Reinforcement Learning from Human Feedback
• [2026/02] Personalized Reasoning: Just-in-time Personalization and Why LLMs Fail at It
• [2026/02] NextQuill: Causal Preference Modeling for Enhancing LLM Personalization
• [2026/02] What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data
• [2026/02] P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling
• [2026/02] Aligning Language Models from User Interactions
• [2025/10] Towards Faithful and Controllable Personalization via Critique-Post-Edit Reinforcement Learning
• [2025/10] POPI: Personalizing LLMs via Optimized Natural Language Preference Inference
• [2025/09] Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations
• [2025/07] PrefPalette: Personalized Preference Modeling with Latent Attributes
• [2025/07] CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering
• [2025/07] PRIME: Large Language Model Personalization with Cognitive Memory and Thought Processes
• [2025/06] Personalized LLM Decoding via Contrasting Personal Preference
• [2025/06] PersonaFeedback: A Large-scale Human-annotated Benchmark For Personalization
• [2025/06] PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants
• [2025/06] SynthesizeMe! Inducing Persona-Guided Prompts for Personalized Reward Models in LLMs
• [2025/06] PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time
• [2025/06] Aligning VLM Assistants with Personalized Situated Cognition
• [2025/06] MiCRo: Mixture Modeling and Context-aware Routing for Personalized Preference Learning
• [2025/05] When Harry Meets Superman: The Role of The Interlocutor in Persona-Based Dialogue Generation
• [2025/05] Reasoning Meets Personalization: Unleashing the Potential of Large Reasoning Model for Personalized Generation
• [2025/05] Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals
• [2025/05] Steering Large Language Models for Machine Translation Personalization
• [2025/05] From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment
• [2025/05] Embodied Agents Meet Personalization: Exploring Memory Utilization for Personalized Assistance
• [2025/05] Teaching Language Models to Evolve with Users: Dynamic Profile Modeling for Personalized Alignment
• [2025/05] A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations
• [2025/04] LoRe: Personalizing LLMs via Low-Rank Reward Modeling
• [2025/04] Persona-judge: Personalized Alignment of Large Language Models via Token-level Self-judgment
• [2025/03] EmpathyAgent: Can Embodied Agents Conduct Empathetic Actions?
• [2025/03] From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment
• [2025/03] Toward Multi-Session Personalized Conversation: A Large-Scale Dataset and Hierarchical Tree Framework for Implicit Reasoning
• [2025/02] Drift: Decoding-time Personalized Alignments with Implicit User Preferences
• [2025/02] When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning
• [2025/02] Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs
• [2025/02] PEToolLLM: Towards Personalized Tool Learning in Large Language Models
• [2025/02] Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs
• [2024/09] Everyone Deserves A Reward: Learning Customized Human Preferences
• [2024/12] AI PERSONA: Towards Life-long Personalization of LLMs
• [2024/11] BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
• [2024/11] The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
• [2024/10] Large Language Models Empowered Personalized Web Agents
• [2024/10] ComPO: Community Preferences for Language Model Personalization
• [2024/10] MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time
• [2024/10] LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Education
• [2024/10] Personalized Adaptation via In-Context Preference Learning
• [2024/10] Aligning LLMs with Individual Preferences via Interaction
• [2024/10] Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements
• [2024/10] PAD: Personalized Alignment at Decoding-Time
• [2024/10] MAP: Multi-Human-Value Alignment Palette
• [2024/10] PAL: Sample-Efficient Personalized Reward Modeling for Pluralistic Alignment
• [2024/09] PersonalLLM: Tailoring LLMs to Individual Preferences
• [2024/08] Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement
• [2024/08] Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning
• [2024/06] Show, Don't Tell: Aligning Language Models with Demonstrated Feedback
• [2024/06] Few-shot Personalization of LLMs with Mis-aligned Responses
• [2024/06] Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration
• [2024/05] Aligning to Thousands of Preferences via System Message Generalization
• [2024/05] RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation
• [2024/04] The PRISM Alignment Project: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
• [2024/02] Personalized Language Modeling from Personalized Human Feedback
• [2023/10] Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging