Summary of Spotting Llms with Binoculars: Zero-shot Detection Of Machine-generated Text, by Abhimanyu Hans et al.
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text
by Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 In this study, researchers aim to develop an accurate method for detecting text generated by modern large language models (LLMs). They propose a novel approach called Binoculars that uses two pre-trained LLMs and simple calculations to separate human-generated from machine-generated text. The method achieves state-of-the-art accuracy without any training data and can detect over 90% of generated samples from ChatGPT and other LLMs at a false positive rate of 0.01%. This approach has implications for various applications, including content moderation, plagiarism detection, and natural language processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better and better at generating text that looks like it was written by humans. But how do we tell the difference between human-written and machine-generated text? Researchers have developed a new method called Binoculars that uses two special language models to detect when someone is using a large language model to generate text. This method is very accurate and can spot machine-generated text even if it’s hard to tell apart from human-written text. |
Keywords
* Artificial intelligence * Large language model * Natural language processing
