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Computer Science > Machine Learning

arXiv:2311.17035 (cs)
[Submitted on 28 Nov 2023]

Title:Scalable Extraction of Training Data from (Production) Language
Models

Authors:Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew
Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A.
Choquette-Choo, Eric Wallace, Florian Tramer, Katherine Lee
Download a PDF of the paper titled Scalable Extraction of Training
Data from (Production) Language Models, by Milad Nasr and 9 other
authors
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    Abstract:This paper studies extractable memorization: training
    data that an adversary can efficiently extract by querying a
    machine learning model without prior knowledge of the training
    dataset. We show an adversary can extract gigabytes of training
    data from open-source language models like Pythia or GPT-Neo,
    semi-open models like LLaMA or Falcon, and closed models like
    ChatGPT. Existing techniques from the literature suffice to
    attack unaligned models; in order to attack the aligned ChatGPT,
    we develop a new divergence attack that causes the model to
    diverge from its chatbot-style generations and emit training data
    at a rate 150x higher than when behaving properly. Our methods
    show practical attacks can recover far more data than previously
    thought, and reveal that current alignment techniques do not
    eliminate memorization.

Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL);
          Cryptography and Security (cs.CR)
Cite as:  arXiv:2311.17035 [cs.LG]
          (or arXiv:2311.17035v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2311.17035
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Nicholas Carlini [view email]
[v1] Tue, 28 Nov 2023 18:47:03 UTC (2,815 KB)
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