用语义检索提升检测能力，有效识别改写过的大模型文本
SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text
- 引入语义检索机制，结合上下文理解增强检测
- 在改写场景下检测准确率显著提升，通用内容也保持稳定
- 适合需要防伪的在线论坛、问答平台等真实场景
大型语言模型（LLMs）的广泛应用催生了对鲁棒文本检测工具的迫切需求，尤其在面对常规避现有方法的重述技术时。为此，我们提出一种新型语义增强型检测框架SEFD，利用基于检索的机制充分挖掘文本语义。该框架通过系统整合检索技术与传统检测器，采用精心设计的检索机制，在覆盖广度与计算效率间取得平衡。我们在在线论坛、问答平台等常见序列文本场景中验证了方法的有效性。大量实验表明，本框架在改写场景下的检测准确率显著提升，同时对标准大模型生成内容仍保持鲁棒性。
原文摘要 · Abstract (English)
The widespread adoption of large language models (LLMs) has created an urgent need for robust tools to detect LLM-generated text, especially in light of \textit{paraphrasing} techniques that often evade existing detection methods. To address this challenge, we present a novel semantic-enhanced framework for detecting LLM-generated text (SEFD) that leverages a retrieval-based mechanism to fully utilize text semantics. Our framework improves upon existing detection methods by systematically integrating retrieval-based techniques with traditional detectors, employing a carefully curated retrieval mechanism that strikes a balance between comprehensive coverage and computational efficiency. We showcase the effectiveness of our approach in sequential text scenarios common in real-world applications, such as online forums and Q\&A platforms. Through comprehensive experiments across various LLM-generated texts and detection methods, we demonstrate that our framework substantially enhances detection accuracy in paraphrasing scenarios while maintaining robustness for standard LLM-generated content.
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