# Editing Across Languages: A Survey of Multilingual Knowledge Editing

arXiv: https://arxiv.org/abs/2505.14393 · HTML: https://mlsanity.com/paper/2505.14393/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 39 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 27 of 27)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [Editing Factual Knowledge in Language Models](https://mlsanity.com/paper/2104.08164/) (2021)
- [Memory-Based Model Editing at Scale](https://mlsanity.com/paper/2206.06520/) (2022)
- [MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions](https://mlsanity.com/paper/2305.14795/) (2023)
- [Can We Edit Factual Knowledge by In-Context Learning?](https://mlsanity.com/paper/2305.12740/) (2023)
- [Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors](https://mlsanity.com/paper/2211.11031/) (2022)
- [Modifying Memories in Transformer Models](https://mlsanity.com/paper/2012.00363/) (2020)
- [PMET: Precise Model Editing in a Transformer](https://mlsanity.com/paper/2308.08742/) (2023)
- [SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings](https://mlsanity.com/paper/2004.08728/) (2020)
- [TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space](https://mlsanity.com/paper/2402.17811/) (2024)
- [The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse](https://mlsanity.com/paper/2402.09656/) (2024)
- [Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing](https://mlsanity.com/paper/2403.07175/) (2024)
- [Learning to Edit: Aligning LLMs with Knowledge Editing](https://mlsanity.com/paper/2402.11905/) (2024)
- [A Survey on Knowledge Editing of Neural Networks](https://mlsanity.com/paper/2310.19704/) (2023)
- [Can Knowledge Editing Really Correct Hallucinations?](https://mlsanity.com/paper/2410.16251/) (2024)
- [Language Anisotropic Cross-Lingual Model Editing](https://mlsanity.com/paper/2205.12677/) (2022)
- [Cross-lingual Editing in Multilingual Language Models](https://mlsanity.com/paper/2401.10521/) (2024)
- [Multilingual Knowledge Editing with Language-Agnostic Factual Neurons](https://mlsanity.com/paper/2406.16416/) (2024)
- [Exploring Alignment in Shared Cross-lingual Spaces](https://mlsanity.com/paper/2405.14535/) (2024)
- [BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning](https://mlsanity.com/paper/2406.17764/) (2024)
- [MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models](https://mlsanity.com/paper/2404.04990/) (2024)
- [Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge](https://mlsanity.com/paper/2502.02173/) (2025)
- [MPN: Leveraging Multilingual Patch Neuron for Cross-lingual Model Editing](https://mlsanity.com/paper/2401.03190/) (2024)
- [MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language](https://mlsanity.com/paper/2409.12257/) (2024)
- [Edit Once, Update Everywhere: A Simple Framework for Cross-Lingual Knowledge Synchronization in LLMs](https://mlsanity.com/paper/2502.14645/) (2025)

## Built on by (most cited first, 2 of 2)

- [An Exploration of Knowledge Editing for Arabic](https://mlsanity.com/paper/2507.09629/) (2025)
- [CLM-Bench: Benchmarking and Analyzing Cross-lingual Misalignment of LLMs in Knowledge Editing](https://mlsanity.com/paper/2601.17397/) (2026)

## Abstract (arXiv)

While Knowledge Editing has been extensively studied in monolingual settings, it remains underexplored in multilingual contexts. This survey systematizes recent research on Multilingual Knowledge Editing (MKE), a growing subdomain of model editing focused on ensuring factual edits generalize reliably across languages. We present a comprehensive taxonomy of MKE methods, covering parameter-based, memory-based, fine-tuning, and hypernetwork approaches. We survey available benchmarks,summarize key findings on method effectiveness and transfer patterns, identify challenges in cross-lingual propagation, and highlight open problems related to language anisotropy, evaluation coverage, and edit scalability. Our analysis consolidates a rapidly evolving area and lays the groundwork for future progress in editable language-aware LLMs.