Cross-Lingual Knowledge Editing in Large Language Models
Jiaan Wang Yunlong Liang Zengkui Sun Yuxuan Cao Jiarong Xu
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.