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arXiv · 2412.11517

DART: An AIGT Detector using AMR of Rephrased Text

Hyeonchu Park·Byungjun Kim·Bugeun Kim
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Abstract

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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Hyeonchu Park, Byungjun Kim, Bugeun Kim. 2025-02-04. DART: An AIGT Detector using AMR of Rephrased Text. https://arxiv.org/abs/2412.11517

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