mlsanityData to 7 Oct 2026

Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning

arXiv 2402.04401 ↗First posted 6 Feb 2024cs.CLcited by 87

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Abstract (arXiv)

Personalization in large language models (LLMs) is increasingly important, aiming to align the LLMs' interactions, content, and recommendations with individual user preferences. Recent advances have highlighted effective prompt design by enriching user queries with non-parametric knowledge through behavior history retrieval and textual profiles. However, these methods faced limitations due to a lack of model ownership, resulting in constrained customization and privacy issues, and often failed to capture complex, dynamic user behavior patterns. To address these shortcomings, we introduce One PEFT Per User (OPPU), employing personalized parameter-efficient fine-tuning (PEFT) modules to store user-specific behavior patterns and preferences. By plugging in personal PEFT parameters, users can own and use their LLMs individually. OPPU integrates parametric user knowledge in the personal PEFT parameters with non-parametric knowledge from retrieval and profiles, adapting LLMs to user behavior shifts. Experimental results demonstrate that OPPU significantly outperforms existing prompt-based methods across seven diverse tasks in the LaMP benchmark. Further studies reveal OPPU's enhanced capabilities in handling user behavior shifts, modeling users at different activity levels, maintaining robustness across various user history formats, and displaying versatility with different PEFT methods.

Key facts. Cited by 87 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Feb 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 23; 2025: 33; 2026: 31.

87 papers.

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  1. 2024Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalizationcited by 203
  2. 2025Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMscited by 142
  3. 2024Towards Safer Large Language Models through Machine Unlearningcited by 134
  4. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  5. 2024Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Effortscited by 71
  6. 2025A Survey of Personalized Large Language Models: Progress and Future Directionscited by 65
  7. 2024Protecting Privacy in Multimodal Large Language Models with MLLMU-Benchcited by 61
  8. 2024HYDRA: Model Factorization Framework for Black-Box LLM Personalizationcited by 59
  9. 2025Evaluating LLM-based Agents for Multi-Turn Conversations: A Surveycited by 56
  10. 2024Can we Soft Prompt LLMs for Graph Learning Tasks?cited by 52
  11. 2024Machine Unlearning in Generative AI: A Surveycited by 49
  12. 2024Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillationcited by 46
38 more
  1. 2025PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistantscited by 44
  2. 2024Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personascited by 36
  3. 2024AI PERSONA: Towards Life-long Personalization of LLMscited by 30
  4. 2025Personalized Text Generation with Contrastive Activation Steeringcited by 30
  5. 2025O-Mem: Omni Memory System for Personalized, Long Horizon, Self-Evolving Agentscited by 30
  6. 2025Think-While-Generating: On-the-Fly Reasoning for Personalized Long-Form Generationcited by 29
  7. 2024Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsitycited by 27
  8. 2025A Survey of Personalization: From RAG to Agentcited by 26
  9. 2024Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyondcited by 18
  10. 2025Explicit v.s. Implicit Memory: Exploring Multi-hop Complex Reasoning Over Personalized Informationcited by 16
  11. 2026Learning Personalized Agents from Human Feedbackcited by 16
  12. 2025Personalize Your LLM: Fake it then Align itcited by 15
  13. 2026ProAgentBench: Evaluating LLM Agents for Proactive Assistance with Real-World Datacited by 15
  14. 2024Designing AI-Agents with Personalities: A Psychometric Approachcited by 14
  15. 2025PersonaFeedback: A Large-scale Human-annotated Benchmark For Personalizationcited by 13
  16. 2025Personalized LLM Decoding via Contrasting Personal Preferencecited by 12
  17. 2025CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steeringcited by 12
  18. 2025Aligning LLMs by Predicting Preferences from User Writing Samplescited by 11
  19. 2024Understanding the Role of User Profile in the Personalization of Large Language Modelscited by 10
  20. 2026PersonaVLM: Long-Term Personalized Multimodal LLMscited by 10
  21. 2024FlexLLM: Token-Level Co-Serving of LLM Inference and Finetuning with SLO Guaranteescited by 9
  22. 2024Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMscited by 9
  23. 2025EigenLoRAx: Recycling Adapters to Find Principal Subspaces for Resource-Efficient Adaptation and Inferencecited by 9
  24. 2025A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directionscited by 9
  25. 2026One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalizationcited by 9
  26. 2025Personalized Language Models via Privacy-Preserving Evolutionary Model Mergingcited by 8
  27. 2025Impatient Users Confuse AI Agents: High-fidelity Simulations of Human Traits for Testing Agentscited by 8
  28. 2024Learning to Correct for QA Reasoning with Black-box LLMscited by 6
  29. 2025Steering Multimodal Large Language Models Decoding for Context-Aware Safetycited by 6
  30. 2026AlpsBench: An LLM Personalization Benchmark for Real-Dialogue Memorization and Preference Alignmentcited by 6
  31. 2024Panza: Design and Analysis of a Fully-Local Personalized Text Writing Assistantcited by 5
  32. 2025Computational Turing Test Reveals Systematic Differences Between Human and AI Languagecited by 5
  33. 2025Bring My Cup! Personalizing Vision-Language-Action Models with Visual Attentive Promptingcited by 5
  34. 2024Orchestrating LLMs with Different Personalizationscited by 4
  35. 2025Training-free LLM Verification via Recycling Few-shot Examplescited by 4
  36. 2025Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a surveycited by 4
  37. 2026Memory-Induced Tool-Drift in LLM Agentscited by 4
  38. 2024PREDICT: Preference Reasoning by Evaluating Decomposed preferences Inferred from Candidate Trajectoriescited by 3

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