Summary of Enhancing Robustness Of Llm-synthetic Text Detectors For Academic Writing: a Comprehensive Analysis, by Zhicheng Dou et al.
Enhancing Robustness of LLM-Synthetic Text Detectors for Academic Writing: A Comprehensive Analysis
by Zhicheng Dou, Yuchen Guo, Ching-Chun Chang, Huy H. Nguyen, Isao Echizen
First submitted to arxiv on: 16 Jan 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 investigates the emerging large language models, specifically Generative Pre-trained Transformer 4 (GPT-4), which have transformed work and study methods. However, their potential misuse, such as generating academic reports with little human contribution, has garnered significant attention. Researchers have developed detectors to address this issue, prioritizing accuracy on restricted datasets over generalizability. This paper presents a comprehensive analysis of prompt impact on text generated by LLMs, highlighting the lack of robustness in current state-of-the-art GPT detectors. To mitigate these issues, the authors propose Synthetic-Siamese, a reference-based Siamese detector that takes a pair of texts as inquiry and reference. This method improves baseline performances in realistic academic writing scenarios by approximately 67% to 95%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how big language models are changing the way we work and study. These models can generate text on their own, which is useful but also worrying because it could be used to cheat or deceive. Researchers have been trying to develop ways to detect when these models are being misused, but most of them focus on getting good results in special tests rather than making sure they would work well in real-life situations. This paper looks at how different prompts can affect what the models generate and points out a problem with one of the best detectors currently available. To fix this issue, the authors suggest creating a new kind of detector that compares two texts to see if one is generated by a model or not. |
Keywords
» Artificial intelligence » Attention » Gpt » Prompt » Transformer
