AI 中文总结
针对LLM个性化协同写作的需求，提出无训练框架SteerWrite，无需梯度更新即可适配专业领域，在多数据集、指标和模型上实现SOTA性能，大幅降低人工编辑工作量。
AI 中文摘要
尽管大型语言模型（LLMs）在个性化方面展现出巨大潜力，但它们往往缺乏专业领域知识。微调等传统解决方案面临高计算成本和数据快速更新的问题，而检索增强生成无法提供细粒度的令牌级引导。此外，基于聊天的界面仍占主导地位，除编码领域外，生产型协同写作范式尚未得到充分开发。为此，我们推出SteerWrite，这是一个专为个性化协同写作设计的无训练框架。我们的方法无需梯度更新即可有效使基础模型适配专业领域，且针对小数据集做了特定设计。实验表明，SteerWrite在不同数据集、指标和模型上均达到了最先进的性能，显著减少了人工编辑工作量。
英文摘要
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.
