DART通过重述文本语义解析，无须依赖概率特征即可识别多种黑盒AI生成文本。
DART: An AIGT Detector using AMR of Rephrased Text
- 对文本重述后进行语义解析，提取深层语义特征
- 在多类别场景下实现对多种黑盒LLM的准确区分
- 适用于真实世界中未知来源的AI文本检测
随着大语言模型（LLMs）生成的文本越来越接近人类写作，人们对AI生成文本（AIGT）副作用的担忧日益增长。现有检测方法面临两大挑战：一是针对黑盒LLM的检测性能低下，因多数模型依赖概率特征；二是大多数检测器仅在单候选设置下测试，假设已知AIGT来源，与实际场景不符。为此，我们提出DART，包含四个步骤：重述、语义解析、打分和多分类。通过三项实验验证，DART可在不依赖概率特征且无需知晓AIGT来源的情况下，有效区分多种黑盒LLM。
原文摘要 · Abstract (English)
As large language models (LLMs) generate more human-like texts, concerns about the side effects of AI-generated texts (AIGT) have grown. So, researchers have developed methods for detecting AIGT. However, two challenges remain. First, the performance of detecting black-box LLMs is low because existing models focus on probabilistic features. Second, most AIGT detectors have been tested on a single-candidate setting, which assumes that we know the origin of an AIGT and which may deviate from the real-world scenario. To resolve these challenges, we propose DART, which consists of four steps: rephrasing, semantic parsing, scoring, and multiclass classification. We conducted three experiments to test the performance of DART. The experimental result shows that DART can discriminate multiple black-box LLMs without probabilistic features and the origin of AIGT.
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