Summary of Dart: An Aigt Detector Using Amr Of Rephrased Text, by Hyeonchu Park et al.
DART: An AIGT Detector using AMR of Rephrased Text
by Hyeonchu Park, Byungjun Kim, Bugeun Kim
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses concerns about the side effects of AI-generated texts (AIGTs) by developing a novel method called DART. The existing AIGT detectors are limited in their ability to detect black-box large language models (LLMs), which lack probabilistic features. Moreover, most detectors have been tested on single-candidate settings, whereas real-world scenarios often involve multiple candidates. To overcome these challenges, the authors propose a four-step approach: rephrasing, semantic parsing, scoring, and multiclass classification. The DART method is evaluated through three experiments, demonstrating its ability to distinguish between multiple black-box LLMs without probabilistic features and identify the origin of AIGTs. This work has significant implications for natural language processing and AI-generated content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fix a problem with artificial intelligence (AI) generated texts called AI-generated text side effects. Right now, there are only simple methods to detect these side effects, but they don’t work well when the AI models are hard to understand or when we can’t tell where the text came from. The authors propose a new method called DART that solves these problems by breaking down the text into simpler parts and comparing it to other texts. They tested this method with three experiments and found that it works well, even when dealing with multiple AI models and unknown origins. |
Keywords
» Artificial intelligence » Classification » Natural language processing » Semantic parsing
