Microsoft’s MathPrompter Dramatically Improves LLM Performance on Mathematical Reasoning Tasks

Today’s large language models (LLMs) have moved beyond simple language tasks, demonstrating impressive in-context question-answering capabilities informed by novel user-initiated prompting techniques. However, these models cannot provide an accuracy assessment with regard to their responses, and, as Synced previously reported, they tend to struggle with math word problems and reasoning tasks.

In the new paper MathPrompter: Mathematical Reasoning Using Large Language Models, a Microsoft Research team presents MathPrompter, a novel approach that leverages chain-of-thought (CoT) prompting techniques to improve LLM performance on mathematical reasoning problems and increase confidence in their predictions.

This work was inspired by how humans address math questions: breaking the problem into multiple steps and employing various methods to validate each step. To mimic this process for solving such reasoning tasks in an LLM, the researchers turned to zero-shot, chain-of-thought (CoT) prompting techniques.