Training-Free Token-Level Steering for LLM Personalized Co-Writing

Abstract:While Large Language Models (LLMs) show great promise for personalization, they often lack specialized domain knowledge. Conventional solutions like fine-tuning struggle with high computational costs and rapid data updates, while Retrieval-Augmented Generation fails to provide fine-grained, token-level steering. Furthermore, chat-based interfaces remain dominant, whereas productive co-writing paradigms have not yet been well exploited beyond the coding domain. To this end, we introduce SteerWrite, a training-free framework designed for personalized co-writing. Our method effectively adapts the base model to specialized domains without gradient updates, with specific designs tailored to small datasets. Experiments demonstrate that SteerWrite achieves state-of-the-art performance across diverse datasets, metrics, and models, significantly reducing human editing effort.

Computation and Language (cs.CL)

arXiv:2608.06069 [cs.CL]

(or arXiv:2608.06069v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.06069 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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