Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang
| Challenge: | Existing knowledge editing techniques rely on memorizing updated knowledge, impeding LLMs from effectively combining the new knowledge with their inherent knowledge when answering questions. |
| Approach: | They propose a Learning to Edit framework that equips LLMs with the ability to apply updated knowledge to input questions through a two-phase process . |
| Outcome: | The proposed framework outperforms existing methods in knowledge editing tasks and compares it with four benchmarks and two LLM architectures. |
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Knowledge Editing for Large Language Models (2024.lrec-tutorials)
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| Challenge: | Large Language Models (LLMs) are not immune to issues of factual accuracy or logically consistent. |
| Approach: | This tutorial will present cutting-edge methods and practical tools for editing Large Language Models (LLMs). |
| Outcome: | The aim of this course is to familiarize researchers with the latest advancements and emerging strategies in the realm of knowledge editing for LLMs. |
Aligning Language Models with Real-time Knowledge Editing (2026.acl-long)
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| Challenge: | Mainstream knowledge editing methods are static and fail to keep pace with the evolving real-world knowledge. |
| Approach: | They propose a new paradigm for knowledge editing that integrates edit augmentation and self-adaptive post-alignment inference into CRAFT to improve edit success. |
| Outcome: | The proposed method shows significant performance gain on CRAFT and traditional datasets compared to existing methods. |
Knowledge Graph-Driven Memory Editing with Directional Interventions (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are hampered by inaccuracies and outdated information. |
| Approach: | They propose a framework that constructs knowledge graphs using available information to guide the direction of knowledge editing. |
| Outcome: | The proposed framework allows consistent, aligned, and stable information during large-scale editing scenarios. |
Can We Edit LLMs for Long-Tail Biomedical Knowledge? (2025.findings-emnlp)
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| Challenge: | Existing knowledge editing methods can enhance LLMs' performance on long-tail biomedical knowledge, but their performance on high-frequency popular knowledge remains inferior to that on high frequency popular knowledge. |
| Approach: | They conduct the first comprehensive study to investigate the effectiveness of knowledge editing methods for editing long-tail biomedical knowledge. |
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Knowledge Decoupling via Orthogonal Projection for Lifelong Editing of Large Language Models (2025.acl-long)
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| Challenge: | Existing methods for enhancing large language models (LLMs) have achieved some success, but their knowledge understanding and memory capacity significantly degrades after extensive editing. |
| Approach: | They propose a method that stores the basis vectors of the representation space of past edits in a knowledge cache and projects the gradient of the current edit onto a space orthogonal to previous knowledge for updating. |
| Outcome: | The proposed method improves question-answering ability and hallucination mitigation by 14% and 61% for large language models after 3,000 edits. |
Edit Once, Update Everywhere: A Simple Framework for Cross-Lingual Knowledge Synchronization in LLMs (2025.findings-acl)
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| Challenge: | Existing methods to update large language models focus on single-language editing or basic multilingual editing, failing to achieve true cross-linguistic knowledge synchronization. |
| Approach: | They propose a cross-linguistic knowledge democracy edit technique to improve cross-lingual performance. |
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EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models (2024.acl-demos)
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Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. |
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| Outcome: | The proposed framework surpasses traditional fine-tuning in terms of reliability and generalization. |
Editing Across Languages: A Survey of Multilingual Knowledge Editing (2025.emnlp-main)
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| Challenge: | Knowledge Editing is a growing subdomain of model editing focused on ensuring factual edits generalize across languages. |
| Approach: | They present a taxonomy of multilingual knowledge editing methods and benchmarks . authors summarize key findings on method effectiveness and transfer patterns . |
| Outcome: | The proposed methods are compared against available benchmarks and benchmark datasets. |
Robust and Scalable Model Editing for Large Language Models (2024.lrec-main)
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Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, Maosong Sun
| Challenge: | Existing methods that ignore contextual knowledge fail to reliably fall back to parametric knowledge when presented with irrelevant context. |
| Approach: | They propose to use contextual knowledge to update and correct LLMs' knowledge by in-context editing instead of retraining. |
| Outcome: | The proposed method outperforms current state-of-the-art methods by a large margin on a dataset that contains irrelevant questions. |
Enhancing LLM Knowledge Learning through Generalization (2025.findings-emnlp)
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| Challenge: | Continued pre-training on paraphrased data has shown empirical promise for enhancing knowledge acquisition, but this approach is costly and unreliable as it relies on external models or manual effort for rewriting. |
| Approach: | They propose formatting-based data augmentation which diversifies documents conveying the same knowledge by altering document formats rather than their content. |
| Outcome: | The proposed methods improve generalization to diverse paraphrased contexts and enhance pre-training and instruction tuning. |