Knowledge editing aims to change language models' performance on several
special cases (i.e., editing scope) by infusing the corresponding expected
knowledge into them. With the recent advancements in large language models
(LLMs), knowledge editing has been shown as a promising technique to adapt LLMs
to new knowledge without retraining from scratch. However, most of the previous
studies neglect the multi-lingual nature of some main-stream LLMs (e.g., LLaMA,
ChatGPT and GPT-4), and typically focus on monolingual scenarios, where LLMs
are edited and evaluated in the same language. As a result, it is still unknown
the effect of source language editing on a different target language. In this
paper, we aim to figure out this cross-lingual effect in knowledge editing.
Specifically, we first collect a large-scale cross-lingual synthetic dataset by
translating ZsRE from English to Chinese. Then, we conduct English editing on
various knowledge editing methods covering different paradigms, and evaluate
their performance in Chinese, and vice versa. To give deeper analyses of the
cross-lingual effect, the evaluation includes four aspects, i.e., reliability,
generality, locality and portability. Furthermore, we analyze the inconsistent
behaviors of the edited models and discuss their specific challenges.

本文旨在研究知识编辑领域中源语言编辑对不同目标语言的交叉效应，通过将 ZsRE 从英文翻译成中文，构建大规模的跨语言合成数据集，对不同范式的知识编辑方法进行英文和中文的编辑，并评估其在不同方面的性能。进一步分析编辑模型的不一致行为并讨论其挑战。