@inproceedings{liu-etal-2026-representation,
    title = "Representation Interventions Enable Lifelong Knowledge Memory Control in {LLM}s",
    author = "Liu, Xuyuan  and
      Chen, Shengyu  and
      Dong, Xinshuai  and
      Liu, Yanchi  and
      Zhao, Xujiang  and
      Wang, Haoyu  and
      Yan, Yujun  and
      Chen, Haifeng  and
      Chen, Zhengzhang",
    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.246/",
    pages = "5414--5436",
    ISBN = "979-8-89176-390-6",
    abstract = "Large language models (LLMs) often produce incorrect or outdated content. Updating their knowledge efficiently and accurately without costly retraining is a major challenge. This problem is particularly challenging for complex, unstructured knowledge in lifelong settings, where many edits must coexist without interference. We introduce **RILKE** (**R**epresentation **I**ntervention for **L**ifelong **K**nowledg**E** Control), a robust and scalable method that treats knowledge control as interventions within the model{'}s representation space. Leveraging representation-space expressiveness, we identify two key properties enabling RILKE to achieve fine-grained control over complex, unstructured knowledge while maintaining general utility with frozen base weights. During training, RILKE learns paraphrase-robust and edit-localized modules that limit each update to a low-dimensional subspace to minimize cross-edit interference. In inference, a query-adaptive router selects the appropriate module to guide the model{'}s generation. Across LLaMA and Qwen models, RILKE scales effectively to large-scale benchmarks, demonstrating high edit success and strong paraphrase generalization while preserving general utility with modest memory overhead. These results show RILKE is an effective and scalable solution for lifelong knowledge control in LLMs."
}