Summary of Detecting Ai-generated Texts in Cross-domains, by You Zhou et al.
Detecting AI-Generated Texts in Cross-Domains
by You Zhou, Jie Wang
First submitted to arxiv on: 17 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 Medium Difficulty summary: The paper presents RoBERTa-Ranker, a ranking classifier trained on a dataset comprising human-written texts and those generated by large language models (LLMs). The goal is to improve the detection of AI-generated texts in new domains. The researchers fine-tune RoBERTa-Ranker with a small amount of labeled data, achieving better performance compared to popular tools like DetectGPT and GPTZero on both in-domain and cross-domain texts. This approach enables the development of a single system for detecting AI-generated texts across various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: The paper talks about how to better detect computer-written text from human-written text. It trains a special tool called RoBERTa-Ranker using a mix of real and fake texts. Then, it shows how to make this tool work well in new situations by fine-tuning it with just a little more information. This approach helps create one system that can detect AI-generated texts no matter where they come from. |
Keywords
» Artificial intelligence » Fine tuning
