Summary of Detectrl: Benchmarking Llm-generated Text Detection in Real-world Scenarios, by Junchao Wu et al.
DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios
by Junchao Wu, Runzhe Zhan, Derek F. Wong, Shu Yang, Xinyi Yang, Yulin Yuan, Lidia S. Chao
First submitted to arxiv on: 31 Oct 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 The paper presents a new benchmark, DetectRL, for detecting text generated by large language models (LLMs). Despite impressive detection capabilities with zero-shot methods like DetectGPT, the reliability of existing detectors in real-world applications remains underexplored. The study collects human-written datasets from domains where LLMs are prone to misuse and generates data that better aligns with real-world applications. Adversarial LLM-generated text is created using heuristic rules simulating various prompts, human revisions, and writing noises. The results reveal the strengths and limitations of current state-of-the-art (SOTA) detectors and analyze the impact of writing styles, model types, attack methods, text lengths, and real-world human writing factors on different types of detectors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure we can spot when computers are pretending to be humans by generating fake text. Right now, there are some pretty good ways to detect this kind of generated text, but it’s not clear how well they would work in real-life situations. The researchers created a new test to see how these detection methods perform and found that even the best ones still have some weaknesses. They also looked at what makes detecting fake text harder or easier, like different writing styles or types of computer programs used to generate the text. |
Keywords
» Artificial intelligence » Zero shot
