Summary of Chain Of Thought Empowers Transformers to Solve Inherently Serial Problems, by Zhiyuan Li et al.
Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
by Zhiyuan Li, Hong Liu, Denny Zhou, Tengyu Ma
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC); 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 The paper investigates the mechanism behind Chain of Thought (CoT) in Large Language Models (LLMs), showing how it improves accuracy on arithmetic and symbolic reasoning tasks. CoT enables serial computation, a feature lacking in transformer-based models, allowing them to solve problems that were previously out of reach. The authors provide a theoretical understanding of CoT’s power through the lens of expressiveness, demonstrating its effectiveness in solving complex problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper helps us understand how machines can be taught to think more logically and solve complex math problems. It shows that by adding a “chain of thought” mechanism to these machine learning models, they can become much better at doing arithmetic and symbolic reasoning tasks. This is important because it could help machines do all sorts of things that are currently difficult or impossible for them. |
Keywords
* Artificial intelligence * Machine learning * Transformer
