The rise of large language models (LLMs) has created a pressing need to distinguish between human-written and LLM-generated text to ensure authenticity and societal trust. Existing detectors typically provide a binary classification for entire passages, which 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. The Python implementation of our proposal is available at GitHub.
Blogger's Review: This paper effectively introduces change point detection for segmenting human-LLM co-authored texts, showcasing the efficacy of cross-domain techniques in addressing text authenticity issues.
In an era of information overload, such a refined detection method holds significant practical implications. It will be interesting to see how this approach can be validated and optimized in more complex text scenarios in the future.