Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models

TL;DR

This paper introduces a teaching-inspired prompting framework that significantly improves reasoning accuracy in large language models by emulating instructional teaching methods, leading to state-of-the-art results on multiple math benchmarks.

Contribution

The paper proposes a novel teaching-inspired framework for prompting LLMs, incorporating concepts, theorems, and analogous problems to enhance reasoning capabilities, and introduces two new Chinese math datasets.

Findings

Achieves new state-of-the-art performance on four math benchmarks.

Improves reasoning accuracy of GPT-4 with the proposed framework.

Demonstrates effectiveness across nine diverse benchmarks.

Abstract

Large Language Models (LLMs) exhibit impressive performance across various domains but still struggle with arithmetic reasoning tasks. Recent work shows the effectiveness of prompt design methods in enhancing reasoning capabilities. However, these approaches overlook crucial requirements for prior knowledge of specific concepts, theorems, and tricks to tackle most arithmetic reasoning problems successfully. To address this issue, we propose a novel and effective Teaching-Inspired Integrated Framework, which emulates the instructional process of a teacher guiding students. This method equips LLMs with essential concepts, relevant theorems, and similar problems with analogous solution approaches, facilitating the enhancement of reasoning abilities. Additionally, we introduce two new Chinese datasets, MathMC and MathToF, both with detailed explanations and answers. Experiments are conducted on nine benchmarks which demonstrates that our approach improves the reasoning accuracy of LLMs. With GPT-4 and our framework, we achieve new state-of-the-art performance on four math benchmarks (AddSub, SVAMP, Math23K and AQuA) with accuracies of 98.2% (+3.3%), 93.9% (+0.2%), 94.3% (+7.2%) and 81.1% (+1.2%). Our data and code are available at https://github.com/SallyTan13/Teaching-Inspired-Prompting.