{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spotting-llms-with-binoculars-zero-shot","title":"Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text","arxiv_id":"2401.12070","date":"2024-01-22","proceeding":null,"authors":["Abhimanyu Hans","Avi Schwarzschild","Valeriia Cherepanova","Hamid Kazemi","Aniruddha Saha","Micah Goldblum","Jonas Geiping","Tom Goldstein"],"abstract":"Detecting text generated by modern large language models is thought to be hard, as both LLMs and humans can exhibit a wide range of complex behaviors. However, we find that a score based on contrasting two closely related language models is highly accurate at separating human-generated and machine-generated text. Based on this mechanism, we propose a novel LLM detector that only requires simple calculations using a pair of pre-trained LLMs. The method, called Binoculars, achieves state-of-the-art accuracy without any training data. It is capable of spotting machine text from a range of modern LLMs without any model-specific modifications. We comprehensively evaluate Binoculars on a number of text sources and in varied situations. 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