通过推理增强捕捉用户差异，提升大模型个性化生成效果
Unveiling Inference Scaling for Difference-Aware User Modeling in LLM Personalization
- 引入推理缩放机制，自动识别用户差异特征维度
- 在评论生成任务中多指标优于基线方法
- 适合需要精细用户建模的个性化系统开发者
大语言模型日益融入用户日常，推动个性化输出需求增长。以往工作主要依赖用户自身历史，忽视关键的个体间差异。尽管近期方法尝试建模这些差异，其特征提取常基于固定维度和快速直觉推理（系统1），限制了覆盖范围与粒度。为此，我们提出差异感知推理个性化框架DRP，通过推理缩放重构差异提取机制，使模型自主识别相关差异特征维度，并生成结构化定义与描述，实现对用户差异的缓慢、深思熟虑的推理（系统2）。在个性化评论生成实验中，DRP在多个指标上持续优于基线方法。
原文摘要 · Abstract (English)
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.
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