Training Data Locality and Chain-of-Thought Reasoning in LLMs with Ben Prystawski - #673
Published:Feb 26, 2024 19:17
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode from Practical AI featuring Ben Prystawski, a PhD student researching the intersection of cognitive science and machine learning. The core discussion revolves around Prystawski's NeurIPS 2023 paper, which investigates the effectiveness of chain-of-thought reasoning in Large Language Models (LLMs). The paper argues that the local structure within the training data is the crucial factor enabling step-by-step reasoning. The episode explores fundamental questions about LLM reasoning, its definition, and how techniques like chain-of-thought enhance it. The article provides a concise overview of the research and its implications.
Key Takeaways
- •The podcast episode discusses Ben Prystawski's research on LLM reasoning.
- •The research focuses on the importance of training data locality for chain-of-thought reasoning.
- •The paper was presented at NeurIPS 2023 and explores the effectiveness of step-by-step reasoning.
Reference
“Why think step by step? Reasoning emerges from the locality of experience.”