| Challenge: | Existing models editing techniques (METs) can efficiently update outdated LLMs without retraining.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. |
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Language Anisotropic Cross-Lingual Model Editing (2023.findings-acl)
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| Challenge: | Existing work studies monolingual model editing, which lacks cross-lingual transferability to perform editing simultaneously across languages. |
| Approach: | They propose a framework to naturally adapt monolingual model editing approaches to the cross-lingual scenario using parallel corpus. |
| Outcome: | The proposed framework adapts monolingual model editing approaches to the cross-lingual scenario using parallel corpus and amplifies different subsets of parameters for each language. |
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. |
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. |
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. |
| Outcome: | The proposed method improves cross-lingual performance while maintaining high accuracy in monolingual settings. |
Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance (2025.naacl-industry)
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Somnath Banerjee, Avik Halder, Rajarshi Mandal, Sayan Layek, Ian Soboroff, Rima Hazra, Animesh Mukherjee
| Challenge: | Pretrained language models (PLMs) have revolutionized NLP but amplify linguistic inequities in multilingual applications. |
| Approach: | They evaluate pretrained language models including Mistral, TowerInstruct, OpenHathi, Tamil-Llama, and Kan-Lama across eight languages spanning high-resource and low-resourced settings. |
| Outcome: | The proposed models fail to bridge linguistic divides and are inefficient when compared to other models. |
MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models (2025.coling-main)
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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. |
Thesis Proposal: Targeted and Unified Cross-Lingual Unlearning from Multilingual Language Models (2026.acl-srw)
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| Challenge: | Large language models trained on corpora scraped from the web can reproduce sensitive and copyright-protected data. |
| Approach: | They propose to extend existing benchmarks to multilingual data by compiling parallel translations of question-answer pairs consisting of real-world facts and synthetic personally identifiable information. |
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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. |
LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedback (2024.findings-acl)
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| Challenge: | Recent multilingual models support limited number of human languages due to lack of training data for low resource languages. |
| Approach: | They propose a multilingual multilingual LLM that scales to 100 languages . they use a human feedback dataset and a data set to perform multilingual instruction tuning . |
| Outcome: | The proposed model outperforms its peers on five multilingual benchmarks. |
Cross-Lingual Multi-Hop Knowledge Editing (2024.findings-emnlp)
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| 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. |