@inproceedings{pu-etal-2026-decoding,
    title = "Decoding-Unlearning: Fact Forgetting via Entropy-Guided Inference",
    author = "Pu, Jingwen  and
      Shi, Mingjun  and
      Ren, Xinrui  and
      Wang, Yizhe  and
      Zhang, Xinyu  and
      Wang, Zhaokun  and
      She, Kun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1850/",
    pages = "39834--39860",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) exhibit powerful capabilities but inevitably memorize sensitive information, raising privacy, copyright, and safety concerns. Existing LLM unlearning methods typically rely on updating model parameters. While effective, they are often limited in real-world scenarios: fine-tuning large-scale models is costly, may introduce potential irreversible risks, and depends on both forget and retain datasets, which are often difficult to obtain in full. To address these challenges, an ideal solution is to achieve unlearning at inference time. To this end, we propose SEGUE, a training-free, plug-and-play inference-time unlearning strategy. SEGUE employs a probe to detect queries involving forgettable concepts and applies entropy-guided decoding to suppress target knowledge, enabling controllable non-factual generation while preserving overall model capabilities. Experiments on the MUSE, RWKU, and WMDP datasets, covering copyright, entity, and potential-risk knowledge, show that SEGUE effectively balances sensitive knowledge suppression and generation quality, outperforming existing most inference-time unlearning methods."
}