大模型如何精准“遗忘”敏感数据又不伤能力？这项自校准对齐方案给出新思路。
arXiv:2602.02824v2 Announce Type: replace Abstract: LLM unlearning aims to remove the influence of undesirable knowledge from pretrained language mode…
系统检验机器遗忘算法在极端压力下的鲁棒性，为隐私保护研究划出新基准。
arXiv:2608.22527v1 Announce Type: new Abstract: Recently, machine unlearning, the removal of specific training data influence from a model, has gained…
大模型也需“无痕模式”，Redakto为LLM对话提供隐私保护新方案。
arXiv:2608.18260v1 Announce Type: new Abstract: Large Language Models (LLMs) are being increasingly used in everyday applications. A major challenge i…
用机器遗忘替代昂贵的人类反馈，低成本实现大模型偏好对齐，ICML 2026新思路。
arXiv:2504.06659v2 Announce Type: replace-cross Abstract: Despite advances in Preference Alignment (PA) for Large Language Models (LLMs), mainstream m…
SAUL把锐度感知与增强拉格朗日结合，给机器遗忘难题开了一剂新药方。
arXiv:2608.16249v1 Announce Type: new Abstract: Machine unlearning in Large Language Models (LLMs) faces a critical trade-off between erasing target k…
白盒研究揭示大模型遗忘后的恢复规律，用测量替代优化，为LLM安全删除提供新视角。
arXiv:2608.11408v1 Announce Type: new Abstract: Prior white-box studies show that large language models can retain latent traces of target knowledge a…
多模态大模型遗忘新思路，模型合并实现持续去学习，破解隐私与安全难题。
arXiv:2608.04548v1 Announce Type: cross Abstract: Multimodal large language model (MLLM) unlearning methods have been proposed to remove private, sens…
大模型遗忘难题新解，轨迹引导网络实现精准、可持续的知识擦除，兼顾隐私与性能。
arXiv:2608.03123v1 Announce Type: new Abstract: Machine unlearning aims to eliminate the influence of sensitive data on a model. In the real world, un…
针对多模态大模型遗忘任务中数据不平衡引发的公平性挑战，提出全新基准与评估方法
arXiv:2607.21300v1 Announce Type: cross Abstract: Machine unlearning has emerged as a tool for removing personal data from trained models to comply wi…
RAG系统如何实现知识“遗忘”？这篇IEEE论文探讨机器遗忘与检索增强生成的交叉，让AI既能记住也能忘掉秘密，挑战隐私与性能的平衡。
arXiv:2410.15267v3 Announce Type: replace-cross Abstract: The deployment of large language models (LLMs) like ChatGPT and Gemini has shown their power…
首个评估多模态大模型在私有-公共纠缠下遗忘能力的基准，直击隐私防护痛点。
arXiv:2607.02897v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong capabilities, but they may memorize priva…
一篇立场论文，直指LLM领域滥用“机器遗忘”术语，引发对模型安全与概念严谨性的反思
arXiv:2606.27379v1 Announce Type: cross Abstract: Large language models increasingly face demands to "forget" training data, knowledge, or behaviors d…
大模型「遗忘」只是表面功夫？这项研究揭示输出层面的遗忘并非真正的知识删除，对AI安全与合规清除提出全新拷问。
arXiv:2606.25001v1 Announce Type: cross Abstract: Machine unlearning (MU) is commonly judged by output forgetting, such as low forget-set accuracy or …
提出源无关的代理锚点概念擦除方法，解决多模态大模型隐私遗忘难题。
arXiv:2606.09868v1 Announce Type: cross Abstract: As Multimodal Large Language Models (MLLMs) face growing privacy risks and regulatory constraints, m…
无需保留集！新方法SHRED用自蒸馏+logit降级实现LLM高效遗忘，拒绝灾难性性能下降。
arXiv:2605.07482v2 Announce Type: replace-cross Abstract: Machine unlearning for large language models (LLMs) aims to selectively remove memorized con…
将持续学习与机器遗忘视为对偶问题，提出PURGE算法实现精准数据擦除，思路新颖且实用。
arXiv:2606.03808v1 Announce Type: cross Abstract: We propose PURGE, a machine unlearning algorithm built on a simple but an under-exploited observatio…
LLM的机器遗忘并非真正删除，最新研究揭示了其可逆性，挑战现有安全假设。
arXiv:2505.16831v3 Announce Type: replace-cross Abstract: Unlearning in large language models (LLMs) aims to remove specified data, but its efficacy i…
提出ICED方法，通过可解释概念分解实现视觉语言模型中的概念级遗忘，精准移除目标知识而不影响无关语义。
arXiv:2605.14309v1 Announce Type: cross Abstract: Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance …
多模态大模型遗忘新方法ASRU，结合激活引导与强化学习，提升遗忘后生成质量，更符合实际需求。
arXiv:2605.15687v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) may memorize sensitive cross-modal information during pretr…