Multilingual knowledge editing (MKE) aims to simultaneously revise factual
knowledge across multilingual languages within large language models (LLMs).
However, most existing MKE methods just adapt existing monolingual editing
methods to multilingual scenarios, overlooking the deep semantic connections of
the same factual knowledge between different languages, thereby limiting edit
performance. To address this issue, we first investigate how LLMs represent
multilingual factual knowledge and discover that the same factual knowledge in
different languages generally activates a shared set of neurons, which we call
language-agnostic factual neurons. These neurons represent the semantic
connections between multilingual knowledge and are mainly located in certain
layers. Inspired by this finding, we propose a new MKE method by locating and
modifying Language-Agnostic Factual Neurons (LAFN) to simultaneously edit
multilingual knowledge. Specifically, we first generate a set of paraphrases
for each multilingual knowledge to be edited to precisely locate the
corresponding language-agnostic factual neurons. Then we optimize the update
values for modifying these located neurons to achieve simultaneous modification
of the same factual knowledge in multiple languages. Experimental results on
Bi-ZsRE and MzsRE benchmarks demonstrate that our method outperforms existing
MKE methods and achieves remarkable edit performance, indicating the importance
of considering the semantic connections among multilingual knowledge.

多语种知识编辑 (MKE) 旨在同时修订大型语言模型 (LLMs) 中的事实知识。我们研究了 LLMs 如何表示多语种事实知识，并发现不同语言中的相同事实知识通常激活一组共享的神经元，我们称之为语言不可知事实神经元。基于此发现，我们提出了一种通过定位和修改语言不可知事实神经元来同时编辑多语种知识的新方法。在 Bi-ZsRE 和 MzsRE 基准上的实验结果表明，我们的方法优于现有的 MKE 方法，并取得了显著的编辑性能，表明考虑多语种知识之间的语义连接的重要性。

多语言知识编辑与语种无关的事实神经网络

Multilingual Knowledge Editing with Language-Agnostic Factual Neurons

Knowledge represented in Large Language Models (LLMs) is quite often
incorrect and can also become obsolete over time. Updating knowledge via
fine-tuning is computationally resource-hungry and not reliable, and so
knowledge editing (KE) has developed as an effective and economical alternative
to inject new knowledge or to fix factual errors in LLMs. Although there has
been considerable interest in this area, current KE research exclusively
focuses on the monolingual setting, typically in English. However, what happens
if the new knowledge is supplied in one language, but we would like to query
the LLM in a different language? To address the problem of multilingual
knowledge editing, we propose Retrieval-augmented Multilingual Knowledge Editor
(ReMaKE) to update new knowledge in LLMs. ReMaKE can perform model-agnostic
knowledge editing in multilingual settings. ReMaKE concatenates the new
knowledge retrieved from a multilingual knowledge base with prompts. Our
experimental results show that ReMaKE outperforms baseline knowledge editing
methods by a significant margin and is the first KE method to work in a
multilingual setting. We provide our multilingual knowledge editing dataset
(MzsRE) in 12 languages, which along with code, and additional project
information is available at this https URL

多语言知识编辑通过检索增强的方式，有效地更新大型语言模型中的新知识，较传统的微调方法具有更高性能和多语言适用性。