Multi-Step Reasoning with Large Language Models, a Survey

Understand

Large language models (LLMs) with billions of parameters exhibit in-context learning abilities, enabling few-shot learning on tasks that the model was not specifically trained for.

• Traditional models achieve breakthrough performance on language tasks, but do not perform well on basic reasoning benchmarks.
• However, a new in-context learning approach, Chain-of-thought, has demonstrated strong multi-step reasoning abilities on these benchmarks.
• The research on LLM reasoning abilities started with the question whether LLMs can solve grade school math word problems, and has expanded to other tasks in the past few years.