Unveiling Inference Scaling for Difference-Aware User Modeling in LLM Personalization

• URL: http://arxiv.org/abs/2511.15389v1
• Date: Wed, 19 Nov 2025 12:35:40 GMT
• Title: Unveiling Inference Scaling for Difference-Aware User Modeling in LLM Personalization
• Authors: Suyu Chen, Yimeng Bai, Yulong Huang, Xiaoyan Zhao, Yang Zhang,
• Abstract summary: Difference-aware Reasoning Personalization is a framework that reconstructs the difference extraction mechanism by leveraging inference scaling to enhance personalization.<n>LLMs autonomously identifies relevant difference feature dimensions and generates structured definitions and descriptions, enabling slow, deliberate reasoning (System-2 thinking) over user differences.
• Score: 8.34180795290891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) are increasingly integrated into users' daily lives, driving a growing demand for personalized outputs. Prior work has primarily leveraged a user's own history, often overlooking inter-user differences that are critical for effective personalization. While recent methods have attempted to model such differences, their feature extraction processes typically rely on fixed dimensions and quick, intuitive inference (System-1 thinking), limiting both the coverage and granularity of captured user differences. To address these limitations, we propose Difference-aware Reasoning Personalization (DRP), a framework that reconstructs the difference extraction mechanism by leveraging inference scaling to enhance LLM personalization. DRP autonomously identifies relevant difference feature dimensions and generates structured definitions and descriptions, enabling slow, deliberate reasoning (System-2 thinking) over user differences. Experiments on personalized review generation demonstrate that DRP consistently outperforms baseline methods across multiple metrics.

Related papers

• Synthetic Interaction Data for Scalable Personalization in Large Language Models [67.31884245564086]
  We introduce a high-fidelity synthetic data generation framework called PersonaGym.<n>Unlike prior work that treats personalization as static persona-preference pairs, PersonaGym models a dynamic preference process.<n>We release PersonaAtlas, a large-scale, high-quality, and diverse synthetic dataset of high-fidelity multi-turn personalized interaction trajectories.
  arXiv  Detail & Related papers  (2026-02-12T20:41:22Z)
• CURP: Codebook-based Continuous User Representation for Personalized Generation with LLMs [60.867541073274715]
  We propose a novel framework CURP, which employs a bidirectional user encoder and a discrete prototype codebook to extract multi-dimensional user traits.<n>This design enables plug-and-play personalization with a small number of trainable parameters.<n>We show that CURP achieves superior performance and generalization compared to strong baselines.
  arXiv  Detail & Related papers  (2026-01-31T14:13:06Z)
• Unified Personalized Understanding, Generating and Editing [54.5563878110386]
  We present textbf OmniPersona, an end-to-end personalization framework for unified LMMs.<n>It integrates personalized understanding, generation, and image editing within a single architecture.<n>Experiments demonstrate that OmniPersona delivers competitive and robust performance across diverse personalization tasks.
  arXiv  Detail & Related papers  (2026-01-11T15:46:34Z)
• Latent Inter-User Difference Modeling for LLM Personalization [42.29204853138223]
  Difference-aware Embedding-based Personalization (DEP) is a framework that models inter-user differences in the latent space instead of relying on language prompts.<n>A sparse autoencoder then filters and compresses both user-specific and difference-aware embeddings, preserving only task-relevant features.
  arXiv  Detail & Related papers  (2025-07-28T14:00:57Z)
• PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process [6.27203886967197]
  Large language model (LLM) personalization aims to align model outputs with individuals' unique preferences and opinions.<n>We introduce a unified framework, PRIME, using episodic and semantic memory mechanisms.<n>Experiments validate PRIME's effectiveness across both long- and short-context scenarios.
  arXiv  Detail & Related papers  (2025-07-07T01:54:34Z)
• PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time [87.99027488664282]
  PersonaAgent is a framework designed to address versatile personalization tasks.<n>It integrates a personalized memory module and a personalized action module.<n>Test-time user-preference alignment strategy ensures real-time user preference alignment.
  arXiv  Detail & Related papers  (2025-06-06T17:29:49Z)
• RPM: Reasoning-Level Personalization for Black-Box Large Language Models [13.102489006219548]
  This work introduces reasoning-level personalization as a new paradigm.<n> RPM is the first systematic framework designed to guide the model's reasoning process using structured rationales constructed from patterns in a user's behavior.
  arXiv  Detail & Related papers  (2025-05-27T12:06:16Z)
• Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization [68.79814761867314]
  We propose Difference-aware Personalization Learning (DPL) to enhance Large Language Models (LLMs) personalization.<n>DPL strategically selects representative users for comparison and establishes a structured standard to extract task-relevant differences.<n>Experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization.
  arXiv  Detail & Related papers  (2025-03-04T09:53:26Z)
• Personalized Preference Fine-tuning of Diffusion Models [75.22218338096316]
  We introduce PPD, a multi-reward optimization objective that aligns diffusion models with personalized preferences.<n>With PPD, a diffusion model learns the individual preferences of a population of users in a few-shot way.<n>Our approach achieves an average win rate of 76% over Stable Cascade, generating images that more accurately reflect specific user preferences.
  arXiv  Detail & Related papers  (2025-01-11T22:38:41Z)
• Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes [50.544186914115045]
  Large language models (LLMs) are increasingly embedded in everyday applications.<n> Ensuring their alignment with the diverse preferences of individual users has become a critical challenge.<n>We present a novel framework for few-shot steerable alignment.
  arXiv  Detail & Related papers  (2024-12-18T16:14:59Z)
• Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning [36.88126051792774]
  Personalization in large language models (LLMs) is increasingly important.<n>One PEFT Per User (OPPU) employs personalized parameter-efficient fine-tuning (PEFT) modules to store user-specific behavior patterns and preferences.<n>OPPU significantly outperforms existing prompt-based methods across seven diverse tasks in the LaMP benchmark.
  arXiv  Detail & Related papers  (2024-02-06T21:03:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.