Summary of Locate-then-edit For Multi-hop Factual Recall Under Knowledge Editing, by Zhuoran Zhang et al.
Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
by Zhuoran Zhang, Yongxiang Li, Zijian Kan, Keyuan Cheng, Lijie Hu, Di Wang
First submitted to arxiv on: 8 Oct 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 abstract presents a novel approach to knowledge editing (KE) in Large Language Models (LLMs), specifically addressing the challenges posed by multi-hop factual recall tasks. Current methods excel at single-hop fact recall but struggle with multi-hop tasks that involve newly edited knowledge. The authors identify that LLMs tend to retrieve knowledge from deeper MLP layers in multi-hop tasks, unlike single-hop tasks which rely on shallow layers. To overcome these limitations, the proposed IFMET approach locates and edits both shallow and deep MLP layers using locate-then-edit KE. Experimental results demonstrate significant performance improvements on multi-hop factual recall tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to edit knowledge in computers that can understand human language (LLMs). The old ways of editing work well when the computer only needs to find one piece of information, but they struggle when it needs to find multiple pieces. The authors found out why this happens and created a new method called IFMET that can handle these multi-step tasks better. It’s an important step forward in helping computers understand and edit complex knowledge. |
Keywords
» Artificial intelligence » Recall
