AKEW: Assessing Knowledge Editing in the Wild

Xiaobao Wu; Liangming Pan; William Yang Wang; Anh Tuan Luu

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arXiv · 2402.18909

AKEW: Assessing Knowledge Editing in the Wild

Xiaobao Wu·Liangming Pan·William Yang Wang·Anh Tuan Luu

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Abstract

Knowledge editing injects knowledge updates into language models to keep them correct and up-to-date. However, its current evaluations deviate significantly from practice: their knowledge updates solely consist of structured facts derived from meticulously crafted datasets, instead of practical sources -- unstructured texts like news articles, and they often overlook practical real-world knowledge updates. To address these issues, in this paper we propose AKEW (Assessing Knowledge Editing in the Wild), a new practical benchmark for knowledge editing. AKEW fully covers three editing settings of knowledge updates: structured facts, unstructured texts as facts, and extracted triplets. It further introduces new datasets featuring both counterfactual and real-world knowledge updates. Through extensive experiments, we demonstrate the considerable gap between state-of-the-art knowledge-editing methods and practical scenarios. Our analyses further highlight key insights to motivate future research for practical knowledge editing.

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Xiaobao Wu, Liangming Pan, William Yang Wang, Anh Tuan Luu. 2024-02-29. AKEW: Assessing Knowledge Editing in the Wild. https://arxiv.org/abs/2402.18909

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