AKEW : Assessing Knowledge Editing in the Wild (2024)

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.

## introduction recently large language models (llms) have revolutionized the nlp field and derived various applications @xcite . but a critical challenge arises that their knowledge could become incorrect or outdated over time @xcite @xcite . for this challenge, fine-tuning llms like continual learning @xcite @xcite is feasible, but often requires intensive computational cost and may cause forgetting issues @xcite . due to this, knowledge editing has been proposed @xcite . compared to early continual learning, knowledge editing seeks to inject target knowledge into language models efficiently at low cost and assesses whether the edited models recall these new knowledge @xcite . a wide range of knowledge-editing methods have been proposed and demonstrated their effectiveness @xcite . however, despite these achievements, we argue that current evaluations for knowledge editing deviate significantly from practice. first, their knowledge updates exclusively rely on well-curated structured facts-a single isolated triplet (s, r, o) with subject (s), relation (r), and object (o)-sourced from meticulously crafted datasets @xcite . as such, they overlook the practical sources of knowledge updates-unstructured texts, such as scientific papers and news articles, where knowledge updates commonly emerge in practice @xcite . second, they often lack evaluations on practical real-world knowledge updates, while focusing on fabricated counterfacts @xcite . in consequence, current evaluations fail to consider the diversity and complexity challenge of practical knowledge editing. this inspires us to question: how do current knowledge-editing methods perform in practice? to answer this question, we propose akew (assessing knowledge editing in the wild), a new and comprehensive benchmark that evaluates knowledge-editing methods in practice. as shown in figure 1 , akew covers three editing settings of knowledge updates for complete assessments: (i) structured fact, including a single isolated triplet as in previous studies, like the one in figure 1. (ii) unstructured fact, an unstructured text containing the same knowledge in the structured fact, such as the biography of rishi sunak in figure 1 . (iii) extracted triplets from the unstructured fact, like the triplets extracted from the biography in figure 1 . based on the above settings, akew introduces three new datasets for evaluation: two adapted from previous work include counterfactual knowledge updates with llmgenerated unstructured facts, and the new wiki-update contains real-world knowledge updates with unstructured facts retrieved from wikipedia. we evaluate various state-of-the-art knowledgeediting methods. as exemplified in figure 1 , we observe that they commonly excel on structured facts, but fail significantly on unstructured facts. we also find that extracted triplets are helpful to some knowledge-editing methods but they still fall short compared to structured facts. as a result, these experimental results disclose the considerable gap between existing knowledge-editing methods and the practical scenarios, thus emphasizing the urgency for more research into practical knowledge editing. the contributions of this paper can be concluded as follows: others follow the locate-then-edit principle based on the knowledge neuron view of feed-forward networks @xcite @xcite . they locate the parameters related to a knowledge edit and then modify them to inject new knowledge @xcite . rome @xcite locates the parameters related to a knowledge edit and then modify them to inject new knowledge; memit extends it by enabling mass editing @xcite . recently, @xcite propose to refine the updating of ffn for more precise editing. alternatively, other knowledge-editing approaches choose to preserve model parameters. de @xcite use hyper-networks to model the parameter updates of language models @xcite . mend @xcite transforms the gradients computed by fine-tuning into a low-rank decomposition. some studies follow memory-based strategies to avoid fine-tuning @xcite . serac @xcite stores editing facts in memory and builds a counterfactual model to handle the input queries that fall within the editing scope. following this line, ike @xcite adopts in-context learning via editing exemplars and retrieves related facts in the memory. mello @xcite solves multihop editing by breaking down a multi-hop question into subquestions and retrieving related facts. as such, these two convert knowledge editing into a retrieval-augmented generation (rag) task. knowledge-editing evaluations current evaluations of knowledge editing mainly use wellcurated structured facts as edits, each of which includes a single isolated triplet @xcite . these triplets originate from meticulously crafted datasets rather than practical sources. recently @xcite propose to edit with raw documents. however, they only include fine-tuning methods and lack the latest knowledge-editing methods; besides, they merely consider counterfacts as edits, which still deviates from the practice. different from these studies, our akew (assessing knowledge editing in the wild) covers three editing settings of knowledge updates: structured facts, unstructured facts, and extracted triplets. we experiment with state-ofthe-art knowledge-editing methods (modifying or preserving parameters) on both counterfactual and real-world knowledge updates. these differences make our akew a more practical benchmark for knowledge editing. ## assessing knowledge editing in the wild in this section, we analyze the problems of previous knowledge-editing evaluations and propose our new benchmark akew (assessing knowledge editing in the wild). ## dataset construction for akew in this section we construct three new datasets for the akew benchmark, two containing counterfactual updates and one featuring real-world updates. ## experiment in this section, we conduct experiments with the above datasets to evaluate current knowledgeediting methods on the akew benchmark. ## conclusion and future work in this paper, we propose akew, a novel, practical, and comprehensive benchmark for knowledge editing. akew incorporates three editing settings: structured facts, unstructured facts, and extracted triplets, which extensively evaluates how knowledge-editing methods perform in practical scenarios. akew builds three new datasets, featuring both counterfactual and real-world knowledge updates. through extensive experiments, we reveal that existing knowledge-editing methods commonly struggle with unstructured facts, even if assisted by extracted triplets. these findings verify the challenging nature of knowledge editing in practice and thus highlight the necessity of more research into practical knowledge editing. future work may lie in two aspects. for locatethen-edit methods, we should enhance their ability to edit with multiple related facts at once. they excel with single isolated edits but often fail to edit these complex facts, which greatly hinders their applications. for in-context learning editing methods, we should address their two limitations. despite their commendable performance, they are limited by the critical retrieval success rates, especially when facing complicated real-world knowledge updates. besides, they are limited by the necessity to store new facts in memory. this requires regular and laborious maintenance and becomes more arduous as future facts continue to emerge and evolve over time.