这篇论文给了一个新方法，能精确找出LLM写的片段，不用提前标注，比现有文档级检测更细。
论文提出一种token级检测方法，通过平滑相邻token分数并采用自适应Lepski规则选择带宽，识别人机协作文本中由LLM生成的部分。该方法无需token级标注数据训练，在合成和真实数据集上优于多种基线。作者部署了公开网站实现该方法。
Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM-generated content in mixed-authorship documents. Existing methods for detecting LLM-generated text mainly focus on document-level classification and cannot identify which parts of the text are generated by LLMs. This paper introduces a new method to address this urgent need. Our method operates at the token level, the natural unit of modern language models, and builds on existing token-level detection scores. The key idea is to smooth adjacent token scores to reduce their variability, while using an adaptive Lepski-type rule to select the bandwidth according to the local authorship structure. Our method is simple to implement and does not require token-level labeled data for training. Theoretically, we characterize this trade-off and show that the proposed method achieves favorable mean square error performance in estimating the underlying signal. Empirically, we demonstrate strong performance of our method against a wide range of baselines in both synthetic datasets and a realistic dataset. We deploy a publicly accessible website that implements the methods as well.