Summary of Is Contrasting All You Need? Contrastive Learning For the Detection and Attribution Of Ai-generated Text, by Lucio La Cava et al.
Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text
by Lucio La Cava, Davide Costa, Andrea Tagarelli
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Physics and Society (physics.soc-ph)
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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 proposed WhosAI framework is a triplet-network contrastive learning method designed to detect whether text has been generated by humans or AI and attribute the authorship of the text. The framework learns semantic similarity representations from multiple generators, making it model-agnostic and scalable to new AI text-generation models. Experimental results on the TuringBench benchmark show that WhosAI outperforms existing methods in both the Turing Test and Authorship Attribution tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WhosAI is a special computer program designed to figure out if someone wrote something or if it was written by a machine. The program uses a new way of learning called contrastive learning, which helps it understand how similar different texts are. This means WhosAI can tell the difference between human-written text and AI-generated text, and even identify who wrote a piece of text. The researchers tested WhosAI on a big collection of news articles and found that it did an amazing job! |
Keywords
» Artificial intelligence » Text generation
