用插入无关内容提升短文本生成检测准确率
Short-PHD: Detecting Short LLM-generated Text with Topological Data Analysis After Off-topic Content Insertion
- 在输入前插入无关内容，稳定短文本的拓扑特征
- 在多个数据集上超越现有零样本方法性能
- 适合需要高精度检测短篇生成文本的场景
大型语言模型的滥用促使了对生成文本的检测研究。以往基于拓扑数据分析的工作表明，文本嵌入的持久同调维数（PHD）可作为比其他零样本方法更鲁棒、更有前景的评分指标。然而，有效检测短文本的生成内容仍具挑战。本文提出Short-PHD，一种专为短文本设计的零样本生成文本检测方法。通过在输入前插入无关内容，使先前的PHD方法在短文本上的估计更加稳定，并基于预设阈值识别生成文本。在公开和自动生成数据集上的实验结果表明，Short-PHD在短生成文本检测任务中优于现有零样本方法。代码已公开。
原文摘要 · Abstract (English)
The malicious usage of large language models (LLMs) has motivated the detection of LLM-generated texts. Previous work in topological data analysis shows that the persistent homology dimension (PHD) of text embeddings can serve as a more robust and promising score than other zero-shot methods. However, effectively detecting short LLM-generated texts remains a challenge. This paper presents Short-PHD, a zero-shot LLM-generated text detection method tailored for short texts. Short-PHD stabilizes the estimation of the previous PHD method for short texts by inserting off-topic content before the given input text and identifies LLM-generated text based on an established detection threshold. Experimental results on both public and generated datasets demonstrate that Short-PHD outperforms existing zero-shot methods in short LLM-generated text detection. Implementation codes are available online.
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