Summary of A Theory For Length Generalization in Learning to Reason, by Changnan Xiao and Bing Liu
A Theory for Length Generalization in Learning to Reason
by Changnan Xiao, Bing Liu
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 In this research paper, the authors investigate length generalization (LG), a critical challenge in learning to reason. Specifically, they explore how models struggle with larger problem sizes after being trained on smaller ones. The study proposes a theoretical framework for understanding and addressing LG when reasoning processes can be modeled as directed acyclic graphs (DAGs). Using this theory, the authors design problem representations and employ a Transformer model to achieve perfect LG in solving complex reasoning tasks like parity, addition, and multiplication. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machines struggle to solve bigger problems after learning from smaller ones. The researchers want to understand why this happens and develop ways to fix it. They focus on a special type of problem where the solution involves a series of steps, and they use a powerful machine learning model called a Transformer to help them achieve perfect results in solving these types of problems. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Transformer
