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Computer Science > Machine Learning

arXiv:2506.03142 (cs)
[Submitted on 3 Jun 2025]

Title:Not All Tokens Are Meant to Be Forgotten

Authors:Xiangyu Zhou, Yao Qiang, Saleh Zare Zade, Douglas Zytko,
Prashant Khanduri, Dongxiao Zhu
View a PDF of the paper titled Not All Tokens Are Meant to Be
Forgotten, by Xiangyu Zhou and 5 other authors
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    Abstract:Large Language Models (LLMs), pre-trained on massive
    text corpora, exhibit remarkable human-level language
    understanding, reasoning, and decision-making abilities. However,
    they tend to memorize unwanted information, such as private or
    copyrighted content, raising significant privacy and legal
    concerns. Unlearning has emerged as a promising solution, but
    existing methods face a significant challenge of over-forgetting.
    This issue arises because they indiscriminately suppress the
    generation of all the tokens in forget samples, leading to a
    substantial loss of model utility. To overcome this challenge, we
    introduce the Targeted Information Forgetting (TIF) framework,
    which consists of (1) a flexible targeted information identifier
    designed to differentiate between unwanted words (UW) and general
    words (GW) in the forget samples, and (2) a novel Targeted
    Preference Optimization approach that leverages Logit Preference
    Loss to unlearn unwanted information associated with UW and
    Preservation Loss to retain general information in GW,
    effectively improving the unlearning process while mitigating
    utility degradation. Extensive experiments on the TOFU and MUSE
    benchmarks demonstrate that the proposed TIF framework enhances
    unlearning effectiveness while preserving model utility and
    achieving state-of-the-art results.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2506.03142 [cs.LG]
          (or arXiv:2506.03142v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2506.03142
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Dongxiao Zhu [view email]
[v1] Tue, 3 Jun 2025 17:59:05 UTC (1,424 KB)
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