Summary of On the Generalization and Adaptation Ability Of Machine-generated Text Detectors in Academic Writing, by Yule Liu et al.
On the Generalization and Adaptation Ability of Machine-Generated Text Detectors in Academic Writing
by Yule Liu, Zhiyuan Zhong, Yifan Liao, Zhen Sun, Jingyi Zheng, Jiaheng Wei, Qingyuan Gong, Fenghua Tong, Yang Chen, Yang Zhang, Xinlei He
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 investigates the generalization and adaptation capabilities of machine-generated text (MGT) detectors in academic writing, addressing concerns about plagiarism and misinformation. The authors construct a large-scale dataset, MGT-Acedemic, comprising over 336 million tokens and 749,000 samples from STEM, Humanities, and Social Sciences fields. They benchmark various detectors for binary classification and attribution tasks, revealing the challenges of attribution tasks. A novel attribution task is introduced, requiring models to adapt to new classes without prior training data in few-shot or many-shot scenarios. Eight adapting techniques are implemented to improve performance, highlighting the complexity of the task. The findings provide insights into MGT detector generalization and adaptation across diverse scenarios, laying the foundation for building robust detection systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well machines can detect when someone is copying text from another source instead of writing it themselves. This is a big problem in schools and universities because people might copy answers to tests or assignments without giving credit where credit is due. The researchers created a huge collection of texts, including ones written by humans and ones generated by computers, and tested different machines that can detect copied text. They found out that some machines are better at detecting copied text than others, especially when it comes to identifying what parts of the text were copied from someone else. This research helps us understand how machines can get better at detecting copied text and stop plagiarism. |
Keywords
» Artificial intelligence » Classification » Few shot » Generalization
