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Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning
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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.
Forward citations
Cited by 8 Pith papers
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PREF: Reference-Free Evaluation of Personalised Text Generation in LLMs
PREF is a reference-free, two-stage LLM judge that personalizes a quality rubric with a user profile and scores candidates against it, beating reminder-only baselines on the PrefEval implicit preference subset.
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PersonaFeedback: A Large-scale Human-annotated Benchmark For Personalization
PersonaFeedback provides a human-labeled benchmark showing current LLMs, including strong reasoners, score only about 65-70 percent on hard personalization choices, and explicit persona information helps more than retrieval.
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Aligning LLMs by Predicting Preferences from User Writing Samples
PROSE uses iterative refinement and cross-sample verification to infer personalized writing preferences from user demonstrations, outperforming CIPHER by 33% on the new PLUME benchmark.
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From Reddit to Generative AI: Evaluating Large Language Models for Anxiety Support Fine-tuned on Social Media Data
Fine-tuning GPT-3.5 and Llama 2 on r/Anxiety posts improves readability but raises toxicity and bias while reducing empathy and reflection.
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Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals
A 7B model trained with synthetic reasoning demonstrations plus reinforcement learning infers explicit user preference descriptions from behavioral signals, improving personalized response judging and generation.
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Small Is Enough: Per-User Style Rewriting of AI-Edited Text via LoRA Adapters
LoRA-adapted 0.5B-7B language models all reach the same automatic rewriting score (0.69), indicating model size does not change measured quality for this single-user style-rewriting task.
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Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors
Persona-level LoRA fine-tuning lets a 3.8B small language model simulate MovieLens users about as accurately as a much larger frozen LLM, at lower cost.
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Position: It's Time to Act on the Risk of Efficient Personalized Text Generation
Fine-tuned open LLMs can imitate individual writing styles from small samples, evade detection tools, and are not yet addressed by current safeguards or law.
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