Summary of Commonsense Knowledge Editing Based on Free-text in Llms, by Xiusheng Huang et al.
Commonsense Knowledge Editing Based on Free-Text in LLMs
by Xiusheng Huang, Yequan Wang, Jun Zhao, Kang Liu
First submitted to arxiv on: 31 Oct 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 is introduced to address the limitations of current methods in maintaining accurate and timely large language models (LLMs). The proposed solution focuses on commonsense knowledge in free-text form, which is characterized by broad knowledge scope, long content, and non-instantiation. Unlike previous methods that edit single tokens or entities, this method targets the localization and editing of commonsense knowledge within MLP and Attention layers. A Dynamics-aware Editing Method (DEM) is proposed to locate parameter positions corresponding to commonsense knowledge and update it using a Knowledge Editing Module. Experimental results demonstrate the effectiveness of DEM in achieving excellent editing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models need accurate and timely knowledge updates to stay relevant. But current methods are limited because they focus on small pieces of information, not whole sentences or paragraphs. The new approach solves this problem by introducing two techniques: knowledge localization and knowledge editing. The first technique helps identify where commonsense knowledge is stored in the model’s layers. Then, a Dynamics-aware Editing Method updates that knowledge to make it more accurate and up-to-date. |
Keywords
» Artificial intelligence » Attention
