SENTRA通过分析生成文本的下一个词概率序列，精准识别未声明的AI生成内容。
SENTRA: Selected-Next-Token Transformer for LLM Text Detection
- 基于选中下一个词的概率序列构建Transformer检测器
- 在24个领域上超越主流基线，尤其在跨领域场景表现优异
- 适合需要检测隐蔽AI生成文本的平台与安全团队
大语言模型能力日益增强且应用广泛，其被滥用的风险也随之上升。本文针对未明确标注的AI生成文本检测问题，提出一种新型、通用且有监督的检测方法——选中下一个词概率变压器（SENTRA）。SENTRA是一种基于Transformer的编码器，利用选中下一个词的概率序列，并在大量无标签数据上进行对比预训练。在三个主流公开数据集上，覆盖24个文本领域，实验表明，SENTRA作为通用分类器，在跨领域设置下显著优于现有主流基线。
原文摘要 · Abstract (English)
LLMs are becoming increasingly capable and widespread. Consequently, the potential and reality of their misuse is also growing. In this work, we address the problem of detecting LLM-generated text that is not explicitly declared as such. We present a novel, general-purpose, and supervised LLM text detector, SElected-Next-Token tRAnsformer (SENTRA). SENTRA is a Transformer-based encoder leveraging selected-next-token-probability sequences and utilizing contrastive pre-training on large amounts of unlabeled data. Our experiments on three popular public datasets across 24 domains of text demonstrate SENTRA is a general-purpose classifier that significantly outperforms popular baselines in the out-of-domain setting.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。