Edit Once, Update Everywhere: A Simple Framework for Cross-Lingual Knowledge Synchronization in LLM s (2025)

Abstract: Knowledge editing allows for efficient adaptation of large language models (LLMs) to new information or corrections without requiring full retraining. However, prior methods typically focus on either single-language editing or basic multilingual editing, failing to achieve true cross-linguistic knowledge synchronization. To address this, we present a simple and practical state-of-the-art (SOTA) recipe Cross-Lingual Knowledge Democracy Edit (X-KDE), designed to propagate knowledge from a dominant language to other languages effectively. Our X-KDE comprises two stages: (i) Cross-lingual Edition Instruction Tuning (XE-IT), which fine-tunes the model on a curated parallel dataset to modify in-scope knowledge while preserving unrelated information, and (ii) Target-language Preference Optimization (TL-PO), which applies advanced optimization techniques to ensure consistency across languages, fostering the transfer of updates. Additionally, we contribute a high-quality, cross-lingual dataset, specifically designed to enhance knowledge transfer across languages. Extensive experiments on the Bi-ZsRE and MzsRE benchmarks show that X-KDE significantly enhances cross-lingual performance, achieving an average improvement of +8.19%, while maintaining high accuracy in monolingual settings.

## introduction large language models (llms) @xcite @xcite have shown strong capabilities in natural language understanding, generation, and reasoning @xcite @xcite . however, as world knowledge evolves, llms need methods to update outdated information efficiently. knowledge in the former, the editing and verification languages are the same, while in the latter, knowledge is transferred from the source language (e.g., english) to the target language (e.g., chinese). currently, several studies on multilingual knowledge editing have emerged @xcite @xcite @xcite . some of these methods extend the edited language from single to multiple, while others prescribe source-language answers as the ground truth for multilingual queries. both strategies fall short of achieving true cross-lingual knowledge democratization. for example, although ike was regarded as the state-of-the-art method in previous studies @xcite , its performance on the bi-zsre benchmark demonstrates significant limitations, achieving merely a 73.33 average score when editing in english. unlike previous methods that attempt to forcefully correct llm behaviors in both the source and target languages, we propose guiding llms to internalize knowledge from source language editing and apply it to target language queries. our cross-lingual knowledge democracy edit (x-kde) with dual-stage refinement, where we use parallel language datasets to transfer knowledge from the source to the target language. the x-kde framework involves two phases: (i) cross-lingual edition instruction tuning (xe-it), where the source language editing descriptor is paired with target language queries to create a parallel dataset, guiding the model to answer in the target language while preserving unchanged knowledge. (ii) target-language preference optimization (tl-po), where we adopt the orpo strategy @xcite , further constrains crosslingual knowledge, promoting the diffusion of updates from source to target languages, and achieving true knowledge democratization. taking the bi-zsre benchmark as an example, x-kde outperforms others, achieving average scores of 91.04 and 88.49 when editing in english and chinese, respectively. our contributions are three-fold: ## methodology to achieve the democratization of knowledge, we propose the cross-lingual knowledge democracy edit (x-kde) framework, as shown in figure 2 . this framework enables llms to adapt to evolving knowledge demands and facilitates the transfer of knowledge to target languages by editing only the source language. x-kde consists of two stages: the cross-lingual edition instruction tuning (xe-it) phase and the target-language preference optimization (tl-po) phase. ## conclusion in this paper, we present the cross-lingual knowledge democracy edit (x-kde) framework, which facilitates knowledge editing across languages in large language models (llms). by integrating cross-lingual edition instruction tuning (xe-it) and target-language preference optimization (tl-po), x-kde efficiently transfers knowledge from a source language to a target language while maintaining strong performance in monolingual settings. additionally, we introduce high-quality datasets specifically designed for cross-lingual knowledge editing, filling gaps in existing resources. our experimental results demonstrate that x-kde outperforms current methods, offering a scalable solution for cross-lingual knowledge editing. future research will explore applying x-kde to other domains and optimizing its efficiency.