@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://aclanthology.org/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abstract = "Large language models (LLMs) often produce incorrect or outdated content.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."
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<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Large language models (LLMs) often produce incorrect or outdated content.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.</abstract>
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%0 Conference Proceedings
%T Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs
%A Liu, Xuyuan
%A Chen, Shengyu
%A Dong, Xinshuai
%A Liu, Yanchi
%A Zhao, Xujiang
%A Wang, Haoyu
%A Yan, Yujun
%A Chen, Haifeng
%A Chen, Zhengzhang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F liu-etal-2026-representation
%X 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.
%U https://aclanthology.org/2026.acl-long.246/
%P 5414-5436
Markdown (Informal)
[Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs](https://aclanthology.org/2026.acl-long.246/) (Liu et al., ACL 2026)
ACL
- Xuyuan Liu, Shengyu Chen, Xinshuai Dong, Yanchi Liu, Xujiang Zhao, Haoyu Wang, Yujun Yan, Haifeng Chen, and Zhengzhang Chen. 2026. Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5414–5436, San Diego, California, United States. Association for Computational Linguistics.