| 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. |
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| 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. |
Cross-Lingual Knowledge Editing in Large Language Models (2024.acl-long)
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| 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. |
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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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. |
MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions (2023.emnlp-main)
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| Challenge: | Existing methods for retraining from scratch are limited and only work on the recall of edited facts. |
| Approach: | They propose a benchmark method that allows users to ask multi-hop questions to assess whether edited models correctly answer questions where the answer should change as an entailed consequence of edited facts. |
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BABELEDITS: A Benchmark and a Modular Approach for Robust Cross-lingual Knowledge Editing of Large Language Models (2025.findings-acl)
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| Challenge: | Existing methods for cross-lingual knowledge editing are limited in their effectiveness and robustness. |
| Approach: | They propose a new CKE benchmark that accounts for the rich variety of entity aliases within and across languages. |
| Outcome: | The proposed method is more effective than state-of-the-art methods and robust against model collapse when subjected to multiple edits. |
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. |
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Retrieval-Augmented Multilingual Knowledge Editing (2024.acl-long)
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| 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. |
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Robust Knowledge Editing via Explicit Reasoning Chains for Distractor-Resilient Multi-Hop QA (2025.findings-emnlp)
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| Challenge: | Large language models encode vast amounts of knowledge but remain static once trained, making timely integration of emerging facts prohibitively expensive via full retraining. |
| Approach: | They introduce a reasoning-chain-based editing framework that steers a pretrained LLM through four structured stages to filter distractors in a single pass. |
| Outcome: | The proposed framework steers a pretrained LLM through four structured stages to filter distractors in a single pass. |
1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators? (2024.emnlp-main)
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| 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. |