| Challenge: | Large language models embed extensive knowledge and perform exceptionally well across tasks. outdated knowledge or factual errors within LLMs can lead to misleading or incorrect responses. |
| Approach: | They propose to use a dataset to enhance the practicality of model editing to correct inaccurate information within LLMs. |
| Outcome: | The proposed method performs excellently across tasks and scenarios, confirming its practicality. |
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Editing Large Language Models: Problems, Methods, and Opportunities (2023.emnlp-main)
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Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in model editing for LLMs have created challenges and opportunities for the community. |
| Approach: | They propose to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. |
| Outcome: | The proposed method alters behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. |
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. |
Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models (2024.findings-emnlp)
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| Challenge: | Knowledge editing is a promising technique for updating factual knowledge in large language models (LLMs) but studies have identified side effects such as knowledge distortion and the deterioration of general abilities that have emerged after editing. |
| Approach: | They propose to evaluate the side effects of knowledge editing in large language models using metrics and benchmarks. |
| Outcome: | The results of the study highlight the limitations of current knowledge editing methods and outline potential research directions. |
Emptying the Ocean with a Spoon: Should We Edit Models? (2023.findings-emnlp)
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| Challenge: | a recent study has questioned the use of direct model editing for factual corrections in LLMs. aaron s. de stefano, a sociologist, says that model editing is not a systematic remedy for factuality. |
| Approach: | They argue that direct model editing cannot be trusted as a remedy for LLM disadvantages . authors call for cautious promotion and application of model editing as part of LLM deployment process . |
| Outcome: | The proposed method is not trusted as a remedy for the disadvantages inherent to LLMs, the authors argue . they argue that it opens risks by reinforcing the notion that models can be trusted for factuality . |
Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue (2024.emnlp-main)
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| Challenge: | Existing methods that edit large language models with updated knowledge can cause side effects on the general abilities of LLMs such as reasoning, natural language inference, and question answering. |
| Approach: | They propose to regularize the edit update weights by imposing constraints on their complexity based on the RElative Change in weighT. |
| Outcome: | The proposed method can significantly mitigate the side effects while maintaining over 94% editing performance. |
DocMEdit: Towards Document-Level Model Editing (2025.findings-acl)
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| Challenge: | Existing models only output short phrases or sentences, raising doubts about their practical usability. |
| Approach: | They propose a dataset focused on document-level model editing that aims to correct errors and outdated knowledge in Large language models (LLMs) they propose to use document-based model editing to improve model capabilities in real-world scenarios. |
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Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark (2023.findings-acl)
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| Challenge: | Recent model editing techniques can introduce large unwanted side effects, a new study shows . existing specificity benchmarks do not detect these unwanted side-effects . a recent study shows that model edits can cause significant performance drop . |
| Approach: | They extend existing CounterFact benchmark to include a dynamic component and propose a new benchmark to evaluate model editing techniques. |
| Outcome: | The proposed benchmark improves existing benchmarks for specificity and avoids unwanted side effects. |
Cross-lingual Editing in Multilingual Language Models (2024.findings-eacl)
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| Challenge: | Existing models editing techniques (METs) can efficiently update outdated LLMs without retraining. |
| Approach: | They propose a cross-lingual model editing paradigm where a fact is edited in one language and the subsequent update propagation is observed across other languages. |
| Outcome: | The proposed techniques perform well in multilingual models with knowledge stored in multiple languages. |
On the Robustness of Editing Large Language Models (2024.emnlp-main)
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| Challenge: | Existing studies have exhibited impressive success and significant potential. |
| Approach: | They propose to modify the knowledge memory with minimum computational cost while preserving the performance on the retained knowledge. |
| Outcome: | The proposed methods avoid retraining to update the model parameters and have demonstrated promising performance and efficiency. |
Can Factual Opinions Be Edited (Manipulated) in Large Language Models? (2026.acl-long)
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| Challenge: | Existing methods for factual opinion editing focus on atomic facts, ignoring the risks associated with factual opinions. |
| Approach: | They propose a method that achieves opinion–evidence alignment without relying on explicit instructions to edit factual opinions. |
| Outcome: | The proposed method achieves opinion–evidence alignment without relying on explicit instructions. |