SteerX: Disentangled Steering for LLM Personalization

• URL: http://arxiv.org/abs/2510.22256v1
• Date: Sat, 25 Oct 2025 11:26:20 GMT
• Title: SteerX: Disentangled Steering for LLM Personalization
• Authors: Xiaoyan Zhao, Ming Yan, Yilun Qiu, Haoting Ni, Yang Zhang, Fuli Feng, Hong Cheng, Tat-Seng Chua,
• Abstract summary: Large language models (LLMs) have shown remarkable success in recent years, enabling a wide range of applications.<n>A critical factor in building such assistants is personalizing LLMs, as user preferences and needs vary widely.<n>We propose SteerX, a method that isolates preference-driven components from preference-agnostic components.
• Score: 75.89038195784701
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have shown remarkable success in recent years, enabling a wide range of applications, including intelligent assistants that support users' daily life and work. A critical factor in building such assistants is personalizing LLMs, as user preferences and needs vary widely. Activation steering, which directly leverages directions representing user preference in the LLM activation space to adjust its behavior, offers a cost-effective way to align the model's outputs with individual users. However, existing methods rely on all historical data to compute the steering vector, ignoring that not all content reflects true user preferences, which undermines the personalization signal. To address this, we propose SteerX, a disentangled steering method that isolates preference-driven components from preference-agnostic components. Grounded in causal inference theory, SteerX estimates token-level causal effects to identify preference-driven tokens, transforms these discrete signals into a coherent description, and then leverages them to steer personalized LLM generation. By focusing on the truly preference-driven information, SteerX produces more accurate activation steering vectors and enhances personalization. Experiments on two representative steering backbone methods across real-world datasets demonstrate that SteerX consistently enhances steering vector quality, offering a practical solution for more effective LLM personalization.

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