Summary of Leveraging Logical Rules in Knowledge Editing: a Cherry on the Top, by Keyuan Cheng et al.
Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top
by Keyuan Cheng, Muhammad Asif Ali, Shu Yang, Gang Lin, Yuxuan Zhai, Haoyang Fei, Ke Xu, Lu Yu, Lijie Hu, Di Wang
First submitted to arxiv on: 24 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed RULE-KE framework addresses the challenges in Multi-hop Question Answering under knowledge editing by leveraging rule discovery to update knowledge about correlated facts. The current best-performing approaches split questions into sub-questions, but this fails for hard-to-decompose questions and doesn’t account for correlated updates resulting from knowledge edits. RULE-KE augments the performance of existing MQA methods under KE, particularly memory-based solutions. Experimental results show that RULE-KE improves performances by up to 112.9% on memory-based solutions and 92% on parameter-based solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RULE-KE is a new approach for updating knowledge in Multi-hop Question Answering when there are changes to what we know. This is important because our understanding of the world is always changing, and we need a way to keep track of these updates. RULE-KE does this by using “rules” or patterns it finds in the data to update our knowledge about things that are related. |
Keywords
» Artificial intelligence » Question answering
