MQuAKE-Remastered: Multi-Hop Knowledge Editing Can Only Be Advanced with Reliable Evaluations
Shaochen (Henry) Zhong, Yifan Lu, Lize Shao, Bhargav Bhushanam, Xiaocong Du, Yixin Wan, Yucheng Shi, Daochen Zha, Yiwei Wang, Ninghao Liu, Kaixiong Zhou, Shuai Xu
Abstract
Large language models (LLMs) can give out erroneous answers to factually rooted questions either as a result of undesired training outcomes or simply because the world has moved on after a certain knowledge cutoff date. Under such scenarios, knowledge editing often comes to the rescue by delivering efficient patches for such erroneous answers without significantly altering the rests, where many editing methods have seen reasonable success when the editing targets are simple and direct (e.g., "what club does Lionel Messi currently play for?"). However, knowledge fragments like this are often deeply intertwined in the real world, making effectively propagating the editing effect to non-directly related questions a practical challenge (to entertain an extreme example: "What car did the wife of the owner of the club that Messi currently plays for used to get to school in the 80s?"). Prior arts have coined this task as multi-hop knowledge editing with the most popular dataset being MQUAKE, serving as the sole evaluation benchmark for many later proposed editing methods due to the expensive nature of making knowledge editing datasets at scale. In this work, we reveal that up to 33% or 76% of MQUAKE's questions and ground truth labels are, in fact, corrupted in various fashions due to some unintentional clerical or procedural oversights. Our work provides a detailed audit of MQUAKE's error pattern and a comprehensive fix without sacrificing its dataset capacity. Additionally, we benchmarked almost all proposed MQUAKEevaluated editing methods on our post-fix dataset, MQUAKE-REMASTERED. It is our observation that many methods try to overfit the original MQUAKE by exploiting some data-specific properties of MQUAKE. We provide a guideline on how to faithfully approach such datasets and show that a simple, minimally invasive approach can bring excellent editing performance without such exploitation. Please refer to https://github.com/henryzhongsc/MQuAKE-Remastered and supplemental material for assets. * Equal contribution. Work corresponds to Shaochen (Henry) Zhong
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 4e72eeef-71de-41f9-becd-1f3b1682c6afBuilds on4
- Fast Model Editing at ScaleEric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn et al.ICLR 2022 · 527 citations
- Editable Neural NetworksAnton Sinitsin, Vsevolod Plokhotnyuk, Dmitry V. Pyrkin, Sergei Popov et al.ICLR 2020 · 210 citations
- PokeMQA: Programmable knowledge editing for Multi-hop Question AnsweringHengrui Gu, Kaixiong Zhou, Xiaotian Han, Ninghao Liu et al.ACL 2024 · 7 citations
- Knowledge Graph Enhanced Large Language Model EditingMengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren et al.EMNLP 2024 · 5 citations
Related papers
- MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop QuestionsZexuan Zhong, Zhengxuan Wu, Christopher D. Manning, Christopher Potts et al.EMNLP 2023 · 36 citations
- Retrieval-Augmented Multilingual Knowledge EditingWeixuan Wang, Barry Haddow, Alexandra BirchACL 2024
- Can Knowledge Editing Really Correct Hallucinations?Baixiang Huang, Canyu Chen, Xiongxiao Xu, Ali Payani et al.ICLR 2025
- CaKE: Circuit-aware Editing Enables Generalizable Knowledge LearnersYunzhi Yao, Jizhan Fang, Jia-Chen Gu, Ningyu Zhang et al.EMNLP 2025 · 1 citation
- ALEX: A Light Editing-knowledge ExtractorMinghu Wang, Shuliang Zhao, Yuanyuan Zhao, Hongxia XuAAAI 2026
