Segmenting Human-LLM Co-authored Text via Change Point Detection

• URL: http://arxiv.org/abs/2605.03723v1
• Date: Tue, 05 May 2026 13:08:55 GMT
• Title: Segmenting Human-LLM Co-authored Text via Change Point Detection
• Abstract summary: The rise of large language models (LLMs) has created an urgent need to distinguish between human-written and LLM-generated text.<n>Existing detectors typically provide a binary classification for an entire passage.<n>We propose algorithms to segment text into human- and LLM-authored pieces.
• Score: 14.004322032566217
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rise of large language models (LLMs) has created an urgent need to distinguish between human-written and LLM-generated text to ensure authenticity and societal trust. Existing detectors typically provide a binary classification for an entire passage; however, this is insufficient for human--LLM co-authored text, where the objective is to localize specific segments authored by humans or LLMs. To bridge this gap, we propose algorithms to segment text into human- and LLM-authored pieces. Our key observation is that such a segmentation task is conceptually similar to classical change point detection in time-series analysis. Leveraging this analogy, we adapt change point detection to LLM-generated text detection, develop a weighted algorithm and a generalized algorithm to accommodate heterogeneous detection score variability, and establish the minimax optimality of our procedure. Empirically, we demonstrate the strong performance of our approach against a wide range of existing baselines.

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