Summary of Reasoning in Large Language Models: a Geometric Perspective, by Romain Cosentino et al.
Reasoning in Large Language Models: A Geometric Perspective
by Romain Cosentino, Sarath Shekkizhar
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 This paper investigates the reasoning abilities of large language models (LLMs) by analyzing their geometrical understanding. The authors establish a connection between an LLM’s expressive power and the density of its self-attention graphs, which define the intrinsic dimension of inputs to multi-layer perceptron (MLP) blocks. They demonstrate through theoretical analysis and toy examples that higher intrinsic dimensions imply greater expressive capacities of LLMs. Empirical evidence is provided linking this geometric framework to recent advancements in methods enhancing LLM reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well large language models can reason, or think critically. The authors want to understand what makes these models good at certain tasks. They found that the way these models process information is important – it’s like a map showing where they go and why they’re good at some things. They also showed that if this “map” is more complex, the model can do more things well. This helps us understand how to make language models even better at doing tasks that require thought and problem-solving. |
Keywords
» Artificial intelligence » Self attention
