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

arXiv: https://arxiv.org/abs/2503.17003 · HTML: https://mlsanity.com/paper/2503.17003/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 121 of its 216 references that are in the map.
- First posted on arXiv 21 Mar 2025.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 30 of 121)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [A General Language Assistant as a Laboratory for Alignment](https://mlsanity.com/paper/2112.00861/) (2021)
- [Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://mlsanity.com/paper/2404.04475/) (2024)
- [BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset](https://mlsanity.com/paper/2307.04657/) (2023)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023)
- [Safe RLHF: Safe Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2310.12773/) (2023)
- [Prometheus: Inducing Fine-grained Evaluation Capability in Language Models](https://mlsanity.com/paper/2310.08491/) (2023)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline](https://mlsanity.com/paper/2406.11939/) (2024)
- [Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision](https://mlsanity.com/paper/2312.09390/) (2023)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)

## Built on by (most cited first, 30 of 42)

- [PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory](https://mlsanity.com/paper/2512.06688/) (2025)
- [Think-While-Generating: On-the-Fly Reasoning for Personalized Long-Form Generation](https://mlsanity.com/paper/2512.06690/) (2025)
- [Instant Personalized Large Language Model Adaptation via Hypernetwork](https://mlsanity.com/paper/2510.16282/) (2025)
- [Towards Personalized Deep Research: Benchmarks and Evaluations](https://mlsanity.com/paper/2509.25106/) (2025)
- [AlpsBench: An LLM Personalization Benchmark for Real-Dialogue Memorization and Preference Alignment](https://mlsanity.com/paper/2603.26680/) (2026)
- [FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data](https://mlsanity.com/paper/2508.04698/) (2025)
- [POPI: Personalizing LLMs via Optimized Natural Language Preference Inference](https://mlsanity.com/paper/2510.17881/) (2025)
- [Inference-Time Personalized Alignment with a Few User Preference Queries](https://mlsanity.com/paper/2511.02966/) (2025)
- [Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals](https://mlsanity.com/paper/2505.18071/) (2025)
- [PluriHarms: Benchmarking the Full Spectrum of Human Judgments on AI Harm](https://mlsanity.com/paper/2601.08951/) (2026)
- [P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling](https://mlsanity.com/paper/2602.12116/) (2026)
- [AI Alignment Breaks at the Edge](https://mlsanity.com/paper/2602.20042/) (2026)
- [Text as a Universal Interface for Transferable Personalization](https://mlsanity.com/paper/2601.04963/) (2026)
- [AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations](https://mlsanity.com/paper/2601.22440/) (2026)
- [To Whom Do Language Models Align? Measuring Principal Hierarchies Under High-Stakes Competing Demands](https://mlsanity.com/paper/2605.12120/) (2026)
- [Structuring the Space of Sociotechnical Alignment](https://mlsanity.com/paper/2607.01250/) (2026)
- [LUNAR: Benchmarking Personalized Large Language Models on UNiversal User BehAvioR Logs](https://mlsanity.com/paper/2608.05246/) (2026)
- [Classical AI vs. LLMs for Decision-Maker Alignment in Health Insurance Choices](https://mlsanity.com/paper/2510.06093/) (2025)
- [CommunityBench: Benchmarking Community-Level Alignment across Diverse Groups and Tasks](https://mlsanity.com/paper/2601.13669/) (2026)
- [CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference](https://mlsanity.com/paper/2602.20732/) (2026)
- [Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment](https://mlsanity.com/paper/2603.10009/) (2026)
- [One Model for All: Multi-Objective Controllable Language Models](https://mlsanity.com/paper/2604.04497/) (2026)
- [Personalizing LLMs with Binary Feedback: A Preference-Corrected Optimization Framework](https://mlsanity.com/paper/2605.10043/) (2026)
- [Aplaud: Adaptive Personalized Low-Rank Decomposition for User-Specific LLM](https://mlsanity.com/paper/2609.04738/) (2026)
- [Toward Robust Personalized Alignment for LLMs: Mitigating Persona Drift in Multi-Turn Dialogue](https://mlsanity.com/paper/2609.12373/) (2026)
- [RACE-Align: Retrieval-Augmented and Chain-of-Thought Enhanced Preference Alignment for Large Language Models](https://mlsanity.com/paper/2506.02726/) (2025)
- [Situation Graph Prediction for User Perspective Modeling](https://mlsanity.com/paper/2602.13319/) (2026)
- [EpiPersona: Persona Projection and Episode Coupling for Pluralistic Preference Modeling](https://mlsanity.com/paper/2603.28197/) (2026)
- [When to Ask a Question: Understanding Communication Strategies in Generative AI Tools](https://mlsanity.com/paper/2605.11240/) (2026)
- [MATO: Multi-objective Personalized Alignment with Test-time Optimization for Large Language Models](https://mlsanity.com/paper/2605.25342/) (2026)

## Abstract (arXiv)

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.