| Challenge: | Knowledge editing (KE) is an effective and economical alternative to inject new knowledge or to fix factual errors in Large Language Models (LLMs). |
| Approach: | They propose a multilingual knowledge editing method that can be used to update knowledge in LLMs by concatenating new knowledge retrieved from a knowledge base with users’ prompts before querying an LLM. |
| Outcome: | The proposed method outperforms baseline knowledge editing methods by a significant margin and is scalable to real-word application scenarios. |
Similar Papers
Editing Across Languages: A Survey of Multilingual Knowledge Editing (2025.emnlp-main)
Copied to clipboard
| 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. |
Cross-Lingual Knowledge Editing in Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Knowledge editing is a promising technique to adapt large language models to new knowledge without retraining from scratch. |
| Approach: | They propose to use a multilingual dataset to translate a large-scale cross-lingual synthetic dataset from English to Chinese and then to evaluate their performance in Chinese. |
| Outcome: | The proposed method can change model performance on several special cases without retraining from scratch. |
MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Existing studies on knowledge editing focus on monolingual scenarios, neglecting the complexities presented by multilingual contexts and multi-hop reasoning. |
| Approach: | They propose a benchmark to evaluate the adaptability of multilingual knowledge editing methods. |
| Outcome: | The proposed benchmark evaluates the adaptability of multilingual knowledge editing methods across five languages. |
Edit Once, Update Everywhere: A Simple Framework for Cross-Lingual Knowledge Synchronization in LLMs (2025.findings-acl)
Copied to clipboard
| 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. |
| Outcome: | The proposed method improves cross-lingual performance while maintaining high accuracy in monolingual settings. |
Cross-lingual Editing in Multilingual Language Models (2024.findings-eacl)
Copied to clipboard
| 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. |
EMCEE: Improving Multilingual Capability of LLMs via Bridging Knowledge and Reasoning with Extracted Synthetic Multilingual Context (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods emphasize reformulating queries into English, but fail to incorporate language- and culture-specific grounding that is essential for some queries. |
| Approach: | They propose a framework that extracts query-relevant knowledge from the LLM itself. |
| Outcome: | The proposed framework outperforms existing approaches on four multilingual benchmarks covering diverse languages and tasks. |
Learning to Edit: Aligning LLMs with Knowledge Editing (2024.acl-long)
Copied to clipboard
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. |
Cross-Lingual Multi-Hop Knowledge Editing (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Prior work on knowledge editing in monolingual settings focused on a single language, but there are significant gaps in performance between the two settings. |
| Approach: | They propose a cross-lingual multi-hop knowledge editing paradigm for measuring and analyzing the performance of various SoTA knowledge editing techniques in a multilingual setup. |
| Outcome: | The proposed system improves on previous methods in a cross-lingual setting and in English. |
1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have been recognized for their impressive capabilities in natural language processing (NLP). |
| Approach: | They propose a method to enhance the multilingual performance of Large Language Models by aggregating knowledge from diverse languages. |
| Outcome: | The proposed method reduces the performance disparity across languages and offers valuable insights for further exploration. |
Knowledge Editing for Large Language Models (2024.lrec-tutorials)
Copied to clipboard
| 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. |