@inproceedings{khandelwal-etal-2024-cross,
    title = "Cross-Lingual Multi-Hop Knowledge Editing",
    author = "Khandelwal, Aditi  and
      Singh, Harman  and
      Gu, Hengrui  and
      Chen, Tianlong  and
      Zhou, Kaixiong",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-emnlp.701/",
    doi = "10.18653/v1/2024.findings-emnlp.701",
    pages = "11995--12015",
    abstract = "Large language models (LLMs) are often expected to be constantly adapted to new sources of knowledge and knowledge editing techniques aim to efficiently patch the outdated model knowledge, with minimal modification. Most prior works focus on monolingual knowledge editing in English, even though new information can emerge in any language from any part of the world. We propose the Cross-Lingual Multi-Hop Knowledge Editing paradigm, for measuring and analyzing the performance of various SoTA knowledge editing techniques in a cross-lingual setup. Specifically, we create a parallel cross-lingual benchmark, CroLin-MQuAKE for measuring the knowledge editing capabilities. Our extensive analysis over various knowledge editing techniques uncover significant gaps in performance between the cross-lingual and English-centric setting. Following this, we propose a significantly improved system for cross-lingual multi-hop knowledge editing, CLeVer-CKE. CLeVer-CKE is based on a retrieve, verify and generate knowledge editing framework, where a retriever is formulated to recall edited facts and support an LLM to adhere to knowledge edits. We develop language-aware and hard-negative based contrastive losses for improving the cross-lingual and fine-grained fact retrieval and verification process used within this framework. Extensive experiments across three LLMs, eight languages, and two datasets show the CLeVer-CKE{'}s significant gains of up to 30{\%} over prior methods."
}