用变化点检测定位人与大模型共同写作中的文本归属
Segmenting Human-LLM Co-authored Text via Change Point Detection
- 将文本分割类比为时间序列变化点检测，提出适配算法
- 在多种数据集上实现优于现有基线的精准分割性能
- 适合需要精细追踪协作文本来源的研究者与平台
大语言模型（LLMs）的兴起带来了区分人类撰写与模型生成文本的迫切需求，以保障内容真实性和社会信任。现有检测方法通常对整段文本给出二元分类，但难以应对人类与大模型协同创作的文本，其目标是精确定位由人类或模型撰写的特定片段。为此，本文提出算法将文本分割为人类和大模型作者的部分。核心观察是：该任务在概念上类似于时间序列分析中的经典变化点检测。基于此类比，我们适配变化点检测方法，开发了加权算法和广义算法以应对检测得分的异质性，并建立了所提方法的极小极大最优性。实证表明，该方法在多种基准测试中表现优异。项目代码已开源：https://github.com/Mamba413/DetectLLMSegmentation。
原文摘要 · Abstract (English)
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. The python implementation of our proposal is available at https://github.com/Mamba413/DetectLLMSegmentation.
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