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Computer Science > Computation and Language

arXiv:2401.12070 (cs)
[Submitted on 22 Jan 2024]

Title:Spotting LLMs With Binoculars: Zero-Shot Detection of
Machine-Generated Text

Authors:Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova,
Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom
Goldstein
Download a PDF of the paper titled Spotting LLMs With Binoculars:
Zero-Shot Detection of Machine-Generated Text, by Abhimanyu Hans and
7 other authors
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    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. Over a wide range of document types, Binoculars
    detects over 90% of generated samples from ChatGPT (and other
    LLMs) at a false positive rate of 0.01%, despite not being
    trained on any ChatGPT data.

Comments: 20 pages, code available at this https URL
Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:2401.12070 [cs.CL]
          (or arXiv:2401.12070v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2401.12070
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Abhimanyu Hans [view email]
[v1] Mon, 22 Jan 2024 16:09:47 UTC (4,003 KB)
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