Segmenting Human-LLM Co-authored Text via Change Point Detection
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910192030973952 |
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| author | Li, Mengchu Zhu, Jin Li, Jinglai Shi, Chengchun |
| author_facet | Li, Mengchu Zhu, Jin Li, Jinglai Shi, Chengchun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03723 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Segmenting Human-LLM Co-authored Text via Change Point Detection Li, Mengchu Zhu, Jin Li, Jinglai Shi, Chengchun Computation and Language Artificial Intelligence Methodology 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. |
| title | Segmenting Human-LLM Co-authored Text via Change Point Detection |
| topic | Computation and Language Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2605.03723 |