Summary of Mugc: Machine Generated Versus User Generated Content Detection, by Yaqi Xie et al.
MUGC: Machine Generated versus User Generated Content Detection
by Yaqi Xie, Anjali Rawal, Yujing Cen, Dixuan Zhao, Sunil K Narang, Shanu Sushmita
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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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 compares eight traditional machine-learning algorithms to distinguish between machine-generated and human-generated text. The evaluation is conducted on three datasets: Poems, Abstracts, and Essays. The results show that traditional methods can accurately identify machine-generated data, with popular pre-trained models like RoBERT demonstrating effectiveness. The study finds that machine-generated texts tend to be shorter and have less word variety compared to human-generated content. While domain-specific keywords may contribute to detection accuracy, deeper word representations like word2vec can capture subtle semantic variations. Additionally, readability, bias, moral, and affect comparisons reveal differences between machine-generated and human-generated content, including variations in expression styles and potential biases in the data sources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research tries to figure out how well computers can tell apart text that humans wrote from text that was made by machines. They tested eight different ways of doing this on three kinds of texts: poems, abstracts, and essays. The results show that these methods are pretty good at telling the difference. Machine-generated texts tend to be shorter and use fewer words than human-written texts. The study also found that machine-made texts might have biases or reflect certain viewpoints, which could be important to know. |
Keywords
* Artificial intelligence * Machine learning * Word2vec
