Summary of Event-level Knowledge Editing, by Hao Peng et al.
Event-level Knowledge Editing
by Hao Peng, Xiaozhi Wang, Chunyang Li, Kaisheng Zeng, Jiangshan Duo, Yixin Cao, Lei Hou, Juanzi Li
First submitted to arxiv on: 20 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 This paper proposes a new approach to updating large language models (LLMs) called event-level knowledge editing. The authors argue that conventional methods of editing LLMs focus on updating factual knowledge triplets, whereas real-world updates typically occur through the occurrence of new events. They demonstrate that their proposed method improves over conventional triplet-level editing in terms of efficiency and completeness, as a single event edit can lead to updates in multiple entailed knowledge triplets. The authors also highlight the importance of considering the influence of events on future trends when updating LLMs’ knowledge. To facilitate further research, they construct a high-quality benchmark dataset called ELKEN, which consists of 1,515 event edits, 6,449 questions about factual knowledge, and 10,150 questions about future tendencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper updates large language models (LLMs) to keep them from becoming outdated. Usually, this happens when new events occur in the world. The current way of doing this is by updating small groups of facts. But this paper shows that a better way is to update LLMs about specific events. This makes it more efficient and complete. It also helps LLMs learn about future trends. To test different ways of doing this, the authors created a big dataset with many examples of event updates. |
