LLM Reasoning Engine: Specialized Training for Enhanced Mathematical Reasoning

TL;DR

This paper introduces a novel training approach for large language models that enhances their mathematical reasoning skills by diversifying question phrasing and employing specialized training objectives, leading to improved performance on reasoning tasks.

Contribution

The paper proposes a question paraphrase strategy and specialized training objectives to significantly improve LLMs' mathematical reasoning capabilities.

Findings

Improved performance on four mathematical reasoning datasets.

Effectiveness demonstrated across different LLM architectures.

Potential for better real-world mathematical problem-solving applications.

Abstract

Large Language Models (LLMs) have shown remarkable performance in various natural language processing tasks but face challenges in mathematical reasoning, where complex problem-solving requires both linguistic understanding and mathematical reasoning skills. Existing approaches to address this challenge often rely on ensemble methods and suffer from the problem of data scarcity in target domains. In this work, we present a novel method to enhance LLMs' capabilities in mathematical reasoning tasks. Motivated by the need to bridge this gap, our approach incorporates a question paraphrase strategy, which aims at diversifying the linguistic forms of mathematical questions to improve generalization. Additionally, specialized training objectives are employed to guide the model's learning process, focusing on enhancing its understanding of mathematical concepts and reasoning processes. We conduct experiments on four datasets using different LLMs, and demonstrate the effectiveness of our approach in improving LLMs' performance on mathematical reasoning tasks. Our findings underscore the significance of our methodology in the advancement of large language models and its potential implications for real-world applications that require mathematical reasoning abilities.