Summary of Detecting Ai-generated Sentences in Human-ai Collaborative Hybrid Texts: Challenges, Strategies, and Insights, by Zijie Zeng et al.
Detecting AI-Generated Sentences in Human-AI Collaborative Hybrid Texts: Challenges, Strategies, and Insights
by Zijie Zeng, Shiqi Liu, Lele Sha, Zhuang Li, Kaixun Yang, Sannyuya Liu, Dragan Gašević, Guanliang Chen
First submitted to arxiv on: 6 Mar 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 study tackles the challenge of detecting AI-generated sentences within human-AI collaborative hybrid texts. Researchers often rely on synthetic datasets, which typically involve limited and unrealistic scenarios. To better inform real-world applications, this study utilizes the CoAuthor dataset, featuring diverse and realistic hybrid texts generated through multi-turn interactions between human writers and an intelligent writing system. The team adopts a two-step pipeline: segment detection and authorship classification. The findings highlight that detecting AI-generated sentences is challenging due to human writers’ editing and preference-based selecting of AI-generated content, as well as frequent changes in authorship between neighboring sentences. Additionally, the short length of text segments provides limited stylistic cues for reliable authorship determination. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study investigates how to identify AI-made text within texts written by both humans and computers. Currently, researchers use fake datasets that aren’t very realistic. This makes it hard to apply their findings to real-life situations. To solve this problem, the team used a dataset called CoAuthor, which contains many different types of texts created through interactions between human writers and an AI writing system. The researchers divided their process into two steps: first, they detected segments within the text where each segment had sentences written by either humans or computers, and then they figured out who wrote each segment. Their results show that detecting AI-generated text is difficult because humans often edit or choose AI-written content based on their own preferences, making it hard to identify the author of a given segment. |
Keywords
» Artificial intelligence » Classification
