Fints: Efficient Inference-Time Personalization for LLMs with Fine-Grained Instance-Tailored Steering

📝 Original Info

• Title: Fints: Efficient Inference-Time Personalization for LLMs with Fine-Grained Instance-Tailored Steering
• ArXiv ID: 2510.27206
• Date: 2025-10-31
• Authors: ** - 논문에 명시된 저자 정보가 제공되지 않았습니다. (GitHub 레포지토리 URL(https://github.com/KounianhuaDu/Fints)에 기반하면 Kounianhua Du가 주요 연구자일 가능성이 높습니다.) **

📝 Abstract

The rapid evolution of large language models (LLMs) has intensified the demand for effective personalization techniques that can adapt model behavior to individual user preferences. Despite the non-parametric methods utilizing the in-context learning ability of LLMs, recent parametric adaptation methods, including personalized parameter-efficient fine-tuning and reward modeling emerge. However, these methods face limitations in handling dynamic user patterns and high data sparsity scenarios, due to low adaptability and data efficiency. To address these challenges, we propose a fine-grained and instance-tailored steering framework that dynamically generates sample-level interference vectors from user data and injects them into the model's forward pass for personalized adaptation. Our approach introduces two key technical innovations: a fine-grained steering component that captures nuanced signals by hooking activations from attention and MLP layers, and an input-aware aggregation module that synthesizes these signals into contextually relevant enhancements. The method demonstrates high flexibility and data efficiency, excelling in fast-changing distribution and high data sparsity scenarios. In addition, the proposed method is orthogonal to existing methods and operates as a plug-in component compatible with different personalization techniques. Extensive experiments across diverse scenarios--including short-to-long text generation, and web function calling--validate the effectiveness and compatibility of our approach. Results show that our method significantly enhances personalization performance in fast-shifting environments while maintaining robustness across varying interaction modes and context lengths. Implementation is available at https://github.com/KounianhuaDu/Fints.

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