Summary of Akew: Assessing Knowledge Editing in the Wild, by Xiaobao Wu et al.
AKEW: Assessing Knowledge Editing in the Wild
by Xiaobao Wu, Liangming Pan, William Yang Wang, Anh Tuan Luu
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to assessing knowledge editing in language models is proposed, addressing the limitations of current evaluations that solely rely on structured facts from meticulously curated datasets. The AKEW benchmark fills this gap by covering three editing settings: structured facts, unstructured texts as facts, and extracted triplets. Additionally, new datasets are introduced featuring both counterfactual and real-world knowledge updates. Experimental results demonstrate a significant disparity between state-of-the-art methods and practical scenarios, highlighting key insights to guide future research in practical knowledge editing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models need to stay up-to-date with the latest knowledge, but current tests don’t reflect how this is done in real life. Researchers are proposing a new way to evaluate language models’ ability to update their knowledge. This approach looks at three different scenarios: updating facts from structured data, unstructured texts like news articles, and extracting triplets of information. The goal is to create more realistic tests that better represent how language models are used in the real world. By doing this, researchers hope to close the gap between what we currently know and what we can actually achieve with language models. |
