Summary of Concept Boundary Vectors, by Thomas Walker
Concept Boundary Vectors
by Thomas Walker
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper explores how machine learning models learn to represent input data in their latent space. Despite being trained with simple objectives like predicting the next token, these models seem to capture a deeper understanding of the data. To better understand this representation and improve its salience, the authors introduce concept boundary vectors (CBVs) as an alternative to existing concept vector constructions. CBVs are derived from the boundary between latent representations of concepts and empirically show that they effectively capture a concept’s semantic meaning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how machine learning models work by looking at how they represent input data. It turns out that even simple training objectives can lead to complex representations! The authors create something called concept boundary vectors, which are useful for understanding what concepts mean. They show that these new vectors are good at capturing the meaning of a concept. |
Keywords
» Artificial intelligence » Latent space » Machine learning » Token
