Detecting LLM-Generated Tokens in Human--LLM Coauthored Text

• URL: http://arxiv.org/abs/2607.21458v1
• Date: Thu, 23 Jul 2026 16:01:16 GMT
• Title: Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
• Abstract summary: 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.<n>This paper introduces a new method to address this urgent need.<n>Our method operates at the token level, the natural unit of modern language models, and builds on existing token-level detection scores.
• Score: 11.447769496973137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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