# LLMs + Persona-Plug = Personalized LLMs

arXiv: https://arxiv.org/abs/2409.11901 · HTML: https://mlsanity.com/paper/2409.11901/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 37 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 27 of 27)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://mlsanity.com/paper/2212.09741/) (2022)
- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [Beam Search Strategies for Neural Machine Translation](https://mlsanity.com/paper/1702.01806/) (2017)
- [Text Embeddings Reveal (Almost) As Much As Text](https://mlsanity.com/paper/2310.06816/) (2023)
- [Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation](https://mlsanity.com/paper/2404.05970/) (2024)
- [LongLaMP: A Benchmark for Personalized Long-form Text Generation](https://mlsanity.com/paper/2407.11016/) (2024)
- [User-LLM: Efficient LLM Contextualization with User Embeddings](https://mlsanity.com/paper/2402.13598/) (2024)
- [Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts](https://mlsanity.com/paper/2406.10471/) (2024)
- [Personalized Large Language Models](https://mlsanity.com/paper/2402.09269/) (2024)
- [Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion](https://mlsanity.com/paper/2311.06318/) (2023)
- [RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit](https://mlsanity.com/paper/2306.05212/) (2023)
- [Personalized LLM Response Generation with Parameterized Memory Injection](https://mlsanity.com/paper/2404.03565/) (2024)
- [User Embedding Model for Personalized Language Prompting](https://mlsanity.com/paper/2401.04858/) (2024)
- [Large Language Models for User Interest Journeys](https://mlsanity.com/paper/2305.15498/) (2023)
- [INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning](https://mlsanity.com/paper/2401.06532/) (2024)
- [PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Prompting](https://mlsanity.com/paper/2408.00960/) (2024)
- [Step-Back Profiling: Distilling User History for Personalized Scientific Writing](https://mlsanity.com/paper/2406.14275/) (2024)
- [Personalizing Search Results Using Hierarchical RNN with Query-aware Attention](https://mlsanity.com/paper/1908.07600/) (2019)

## Built on by (most cited first, 30 of 32)

- [Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs](https://mlsanity.com/paper/2502.09597/) (2025)
- [A Survey of Personalized Large Language Models: Progress and Future Directions](https://mlsanity.com/paper/2502.11528/) (2025)
- [Can Language Models Reason about Individualistic Human Values and Preferences?](https://mlsanity.com/paper/2410.03868/) (2024)
- [Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent Approach](https://mlsanity.com/paper/2505.18882/) (2025)
- [Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning](https://mlsanity.com/paper/2506.01748/) (2025)
- [PersoBench: Benchmarking Personalized Response Generation in Large Language Models](https://mlsanity.com/paper/2410.03198/) (2024)
- [Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models](https://mlsanity.com/paper/2505.17051/) (2025)
- [Preference Learning for AI Alignment: a Causal Perspective](https://mlsanity.com/paper/2506.05967/) (2025)
- [POPI: Personalizing LLMs via Optimized Natural Language Preference Inference](https://mlsanity.com/paper/2510.17881/) (2025)
- [Your Language Model Secretly Contains Personality Subnetworks](https://mlsanity.com/paper/2602.07164/) (2026)
- [PSPA-Bench: A Personalized Benchmark for Smartphone GUI Agent](https://mlsanity.com/paper/2603.29318/) (2026)
- [Tagging the Thought: Unlocking Personalization Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2509.23140/) (2025)
- [Steer Model beyond Assistant: Controlling System Prompt Strength via Contrastive Decoding](https://mlsanity.com/paper/2601.06403/) (2026)
- [Human Tool: An MCP-Style Framework for Human-Agent Collaboration](https://mlsanity.com/paper/2602.12953/) (2026)
- [Enhancing Zero-shot Personalized Image Aesthetics Assessment with Profile-aware Multimodal LLM](https://mlsanity.com/paper/2604.17233/) (2026)
- [Sparse Mixture-of-Experts Reward Models Learn Interpretable and Specialized Experts for Personalized Preference Modeling](https://mlsanity.com/paper/2606.04284/) (2026)
- [P2VA: Converting Persona Descriptions into Voice Attributes for Fair and Controllable Text-to-Speech](https://mlsanity.com/paper/2505.17093/) (2025)
- [Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization](https://mlsanity.com/paper/2604.00997/) (2026)
- [Self-supervised User Profile Generation for Personalization](https://mlsanity.com/paper/2606.05336/) (2026)
- [Personalised Explanations in Long-term Human-Robot Interactions](https://mlsanity.com/paper/2507.03049/) (2025)
- [PERCEIVE: A Benchmark for Personalized Emotion and Communication Behavior Understanding on Social Media](https://mlsanity.com/paper/2605.12525/) (2026)
- [Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs](https://mlsanity.com/paper/2606.09038/) (2026)
- [From Profiles to Steering Vectors: Global Sparse Priors and Local Semantic Calibration for Personalized Text Generation](https://mlsanity.com/paper/2607.21620/) (2026)
- [Beyond Routing Weights: Faithful Response-Level Interpretation of Mixture-of-Experts Reward Models via Contribution Contrast](https://mlsanity.com/paper/2608.06400/) (2026)
- [Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning](https://mlsanity.com/paper/2608.09507/) (2026)
- [PersonaEdit: Representative Sample Selection for Personalized Model Editing](https://mlsanity.com/paper/2608.27816/) (2026)
- [Bounded Personas Match Retrieval on Classification but Not Regression for a Frozen Agent](https://mlsanity.com/paper/2609.02890/) (2026)
- [Inferring Hidden User Models from the Behavior of Personalized LLM Agents](https://mlsanity.com/paper/2609.03815/) (2026)
- [ProTTT: Learning to Learn Semantic User Memory with Test-Time Training](https://mlsanity.com/paper/2609.32564/) (2026)
- [Unlocking Latent Personalization in LLMs](https://mlsanity.com/paper/2609.33182/) (2026)

## Abstract (arXiv)

Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their individual interests. This has led to the development of various personalized approaches aimed at adapting large language models (LLMs) to generate customized outputs aligned with user preferences. Some of them involve fine-tuning a unique personalized LLM for each user, which is too expensive for widespread application. Alternative approaches introduce personalization information in a plug-and-play manner by retrieving the user's relevant historical texts as demonstrations. However, this retrieval-based strategy may break the continuity of the user history and fail to capture the user's overall styles and patterns, hence leading to sub-optimal performance. To address these challenges, we propose a novel personalized LLM model, PPlug. It constructs a user-specific embedding for each individual by modeling all her historical contexts through a lightweight plug-in user embedder module. By attaching this embedding to the task input, LLMs can better understand and capture user habits and preferences, thereby producing more personalized outputs without tuning their own parameters. Extensive experiments on various tasks in the language model personalization (LaMP) benchmark demonstrate that the proposed model significantly outperforms existing personalized LLM approaches.