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Computer Science > Computation and Language

arXiv:2410.16454 (cs)
[Submitted on 21 Oct 2024]

Title:Does your LLM truly unlearn? An embarrassingly simple approach
to recover unlearned knowledge

Authors:Zhiwei Zhang, Fali Wang, Xiaomin Li, Zongyu Wu, Xianfeng Tang
, Hui Liu, Qi He, Wenpeng Yin, Suhang Wang
View a PDF of the paper titled Does your LLM truly unlearn? An
embarrassingly simple approach to recover unlearned knowledge, by
Zhiwei Zhang and 8 other authors
View PDF HTML (experimental)

    Abstract:Large language models (LLMs) have shown remarkable
    proficiency in generating text, benefiting from extensive
    training on vast textual corpora. However, LLMs may also acquire
    unwanted behaviors from the diverse and sensitive nature of their
    training data, which can include copyrighted and private content.
    Machine unlearning has been introduced as a viable solution to
    remove the influence of such problematic content without the need
    for costly and time-consuming retraining. This process aims to
    erase specific knowledge from LLMs while preserving as much model
    utility as possible. Despite the effectiveness of current
    unlearning methods, little attention has been given to whether
    existing unlearning methods for LLMs truly achieve forgetting or
    merely hide the knowledge, which current unlearning benchmarks
    fail to detect. This paper reveals that applying quantization to
    models that have undergone unlearning can restore the "forgotten"
    information. To thoroughly evaluate this phenomenon, we conduct
    comprehensive experiments using various quantization techniques
    across multiple precision levels. We find that for unlearning
    methods with utility constraints, the unlearned model retains an
    average of 21\% of the intended forgotten knowledge in full
    precision, which significantly increases to 83\% after 4-bit
    quantization. Based on our empirical findings, we provide a
    theoretical explanation for the observed phenomenon and propose a
    quantization-robust unlearning strategy to mitigate this
    intricate issue...

Comments: 21 pages, 2 figures
Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2410.16454 [cs.CL]
          (or arXiv:2410.16454v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2410.16454
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Fali Wang [view email]
[v1] Mon, 21 Oct 2024 19:28:37 UTC (1,232 KB)
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    Zhiwei Zhang and 8 other authors
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