Hattie Zhou: Teaching Algorithmic Reasoning via In-context Learning
Published:Dec 20, 2022 17:04
•1 min read
•ML Street Talk Pod
Analysis
This article highlights Hattie Zhou's research on teaching algorithmic reasoning to large language models (LLMs) using in-context learning and algorithmic prompting. It emphasizes the four key stages of her approach and the significant error reduction achieved. The article also mentions her background and collaborators, providing context and credibility to the research.
Key Takeaways
- •Hattie Zhou's research focuses on teaching algorithmic reasoning to LLMs.
- •The approach uses in-context learning and algorithmic prompting.
- •Four key stages are identified for successful teaching.
- •Significant error reduction was achieved on some tasks.
- •The research has implications for tasks requiring similar reasoning.
Reference
“Hattie identifies and examines four key stages for successfully teaching algorithmic reasoning to large language models (LLMs): formulating algorithms as skills, teaching multiple skills simultaneously, teaching how to combine skills, and teaching how to use skills as tools.”