DART: An AIGT Detector using AMR of Rephrased Text
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| Format: | Preprint |
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2024
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| _version_ | 1866909475584081920 |
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| author | Park, Hyeonchu Kim, Byungjun Kim, Bugeun |
| author_facet | Park, Hyeonchu Kim, Byungjun Kim, Bugeun |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_11517 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DART: An AIGT Detector using AMR of Rephrased Text Park, Hyeonchu Kim, Byungjun Kim, Bugeun Computation and Language Artificial Intelligence 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. |
| title | DART: An AIGT Detector using AMR of Rephrased Text |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.11517 |