arXiv cs.AI· Ruihao Pan, Suhang Wang·· 14 小时前AI 评分38
ICCU：通过模式诱导拒绝规则实现上下文内持续遗忘
ICCU: In-Context Continual Unlearning via Pattern-Induced Refusal Rules
AI 导读
ICCU 是一个上下文内持续遗忘框架，从遗忘数据集诱导出可读的拒绝规则，在推理时以过滤器或系统提示方式应用，无需修改模型参数。规则以顺序无关的并集累积，因此无跨请求干扰，且规则诱导后可丢弃原始遗忘集数据。实验显示 ICCU 能有效抑制目标知识并保持模型效用，可扩展至长序列请求，对改写、跨语言和对抗性提问保持稳健。
正文 · 原文
Abstract:Machine unlearning aims to remove the influence of specific data from trained language models. In real-world deployments, unlearning requests often arrive sequentially, which challenges existing fine-tuning-based methods: fine-tuning each request is costly, accumulates utility loss, and may cause cross-request interference. To address these issues, we propose ICCU (In-Context Continual Unlearning), an in-context continual unlearning framework that induces readable refusal rules from unlearning datasets and applies them at inference time either as a filter or via the system prompt, without modifying model parameters. Because rules are accumulated as an order-independent union, ICCU is compositional and free of cross-request interference, and the original forget-set data can be discarded after rule induction. Extensive experiments show that ICCU effectively suppresses target knowledge while preserving utility, scales across long sequences of requests, and remains robust to paraphrased, cross-lingual, and adversarially framed queries. Our code is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.27138 [cs.AI] |
| (or arXiv:2605.27138v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.27138 arXiv-issued DOI via DataCite |
Submission history
From: Ruihao Pan [view email]
[v1]
Tue, 26 May 2026 15:06:56 UTC (1,001 KB)
[v2]
Sat, 24 Oct 2026 15:44:00 UTC (1,007 KB)
来源：arXiv cs.AI · arxiv.org