Summary of From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-thought Enhances Transformer Sample Efficiency, by Kaiyue Wen et al.
From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency
by Kaiyue Wen, Huaqing Zhang, Hongzhou Lin, Jingzhao Zhang
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Machine Learning (stat.ML)
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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 Medium Difficulty summary: Chain-of-thought (CoT) enhances the reasoning performance of large language models (LLMs), improving sample efficiency even when representation power is sufficient. Our parity-learning setup demonstrates that CoT enables transformers to learn functions within polynomial samples, whereas without CoT, exponential samples are required. Additionally, CoT simplifies learning by introducing sparse sequential dependencies among input tokens and leading to sparse and interpretable attention patterns. We validate our theoretical analysis with synthetic and real-world experiments, confirming that sparsity in attention layers is a key factor of the improvement induced by CoT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper studies how thinking like humans can improve computers’ ability to reason. It shows that using something called “chain-of-thought” (CoT) makes large language models (LLMs) better at solving problems and learning from examples. The research finds that CoT helps LLMs learn faster and more efficiently, even when they have lots of power and resources. This is important because it means we can use machines to help us make sense of complex information and make decisions. |
Keywords
* Artificial intelligence * Attention
