Summary of Hidden Holes: Topological Aspects Of Language Models, by Stephen Fitz et al.
Hidden Holes: topological aspects of language models
by Stephen Fitz, Peter Romero, Jiyan Jonas Schneider
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 delves into the geometry of representation manifolds in autoregressive neural language models trained on raw text data. It introduces concepts from computational algebraic topology to analyze the properties of these manifolds and develops a measure of topological complexity called perforation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study examines the structure of representation spaces in AI language models that learn from plain text. Researchers create new tools to understand these spaces better, which helps them define how complex they are. The main contribution is a way to quantify this complexity, called perforation. |
Keywords
* Artificial intelligence * Autoregressive
