Summary of Does Knowledge Localization Hold True? Surprising Differences Between Entity and Relation Perspectives in Language Models, by Yifan Wei et al.
Does Knowledge Localization Hold True? Surprising Differences Between Entity and Relation Perspectives in Language Models
by Yifan Wei, Xiaoyan Yu, Yixuan Weng, Huanhuan Ma, Yuanzhe Zhang, Jun Zhao, Kang Liu
First submitted to arxiv on: 1 Sep 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 investigation into the differences between entity and relational knowledge in large language models reveals that they cannot be directly transferred or mapped to each other. Contrary to expectations, modifying the entity or relation within a knowledge triplet does not yield equivalent outcomes. The study also employs causal analysis to investigate how relational knowledge is stored in pre-trained models, finding that it is significantly encoded in attention modules, challenging prior research suggesting that MLP weights are the primary storage site for knowledge. This multifaceted nature of knowledge storage highlights the complexity of manipulating specific types of knowledge within these models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have shown great success in understanding human language. Researchers have found that some parts of these models hold special information, like words about people or relationships between things. A new study looked at this “knowledge” and found that it comes in two main forms: knowing things about people (entity knowledge) and knowing how those things relate to each other (relational knowledge). Surprisingly, the study discovered that you can’t just take one type of knowledge and swap it with another. This is important because it helps us understand how these powerful models work and how we can use them to make new discoveries. |
Keywords
» Artificial intelligence » Attention
