跨语言知识编辑方法系统梳理，助力多语大模型可靠更新事实
Editing Across Languages: A Survey of Multilingual Knowledge Editing
- 分类整理参数、记忆、微调与超网络四类跨语言编辑方法
- 发现跨语言知识传播存在语言不对称性，编辑效果受语言对影响
- 适合关注多语大模型可编辑性与评估标准的研究者
尽管单语知识编辑已得到广泛研究，但多语言场景下的知识编辑仍鲜有探索。本文系统综述了多语言知识编辑（MKE）这一快速发展的子领域，聚焦于确保事实编辑在多语言间可靠泛化。我们提出一个涵盖参数法、记忆法、微调和超网络的完整分类体系，梳理现有基准数据集，总结方法有效性与跨语言迁移规律，识别跨语言传播中的挑战，包括语言不对称性、评估覆盖不足与编辑可扩展性问题。分析结果为可编辑的语言感知大模型的未来发展奠定基础。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。