| Challenge: | EMMET is a new batched memory-editing algorithm for Transformers that can perform batched edits up to a batch-size of 10,000. |
| Approach: | They propose to unify ROME and MEMIT under a single umbrella to optimize for the preservation-memorization objective. |
| Outcome: | The proposed algorithms perform identically across multiple dimensions and are comparable in their optimization objective, performance and limitations. |
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| Challenge: | Existing model editing methods are evaluated using metrics for reliability, specificity and generalization over one or few edits. |
| Approach: | They evaluate model editing methods for three crucial properties - editing proficiency, fact forgetting and downstream performance. |
| Outcome: | The proposed methods are based on two state-of-the-art models - ROME and MEMIT. |
MEMIT-Merge: Addressing MEMIT’s Key-Value Conflicts in Same-Subject Batch Editing for LLMs (2025.findings-acl)
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| Challenge: | Existing knowledge editing techniques that modify models’ internal knowledge without full model retraining have gained significant attention. |
| Approach: | They propose an enhanced approach that merges value computation processes for facts sharing the same subject to improve editing efficiency. |
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One Mask to Rule Them All: On Hidden Facts after Editing and How to Find Them (2026.findings-acl)
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| Challenge: | Knowledge editing methods such as ROME and MEMIT update factual associations by modifying MLP weights. |
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Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing (2024.emnlp-main)
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| Challenge: | Recent work using Rank-One Model Editing (ROME) has shown that there are certain facts that the algorithm is unable to edit without breaking the model. |
| Approach: | They propose to use a model editing method called Rank-One Model Editing to make multiple edits to a single model without breaking it. |
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Editing Common Sense in Transformers (2023.emnlp-main)
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Anshita Gupta, Debanjan Mondal, Akshay Sheshadri, Wenlong Zhao, Xiang Li, Sarah Wiegreffe, Niket Tandon
| Challenge: | Currently, the performance of transformer-based model editing methods is limited to statements about encyclopedic knowledge with a single correct answer. |
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Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models (2024.acl-long)
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Zihao Lin, Mohammad Beigi, Hongxuan Li, Yufan Zhou, Yuxiang Zhang, Qifan Wang, Wenpeng Yin, Lifu Huang
| Challenge: | Memory Editing (ME) has emerged as an efficient method to modify erroneous facts or inject new knowledge into Large Language Models (LLMs). |
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Efficient Knowledge Editing via Minimal Precomputation (2025.acl-short)
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| Challenge: | Knowledge editing methods like MEMIT require a one-time but significant computational cost. |
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Editing the Moving World: Model Editing for Video LLMs (2026.acl-long)
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Qian Zhang, Xinye Li, Xiaokai Wu, Junhao Xu, Zhanyue Qin, Qingbin Liu, Junxian Cai, Xi Chen, Bolin Zhang, Zhiying Tu, Dianhui Chu, Xiaoyan Yu, Dianbo Sui
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MULFE: A Multi-Level Benchmark for Free Text Model Editing (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have impressive capabilities in comprehending human language and vast parametric knowledge obtained from large corpora. |
| Approach: | They propose a multi-level benchmark for free text model editing to bridge the gap . they categorize probe queries into three levels of generalization . |
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DistillMIKE: Editing Distillation of Massive In-Context Knowledge Editing in Large Language Models (2024.findings-acl)
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| Challenge: | In-context knowledge editing has shown respectable abilities on knowledge editing in terms of generalization and specificity. |
| Approach: | They propose a novel extension of in-context knowledge editing (IKE) that allows for massive edits to be injected into large language models. |
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