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.

翻译：大型语言模型（LLM）在各种自然语言处理任务中展现出卓越性能，但在数学推理方面仍面临挑战，因为复杂问题求解需要同时具备语言理解和数学推理能力。现有解决这一挑战的方法通常依赖于集成策略，并受限于目标领域的数据稀缺问题。本研究提出一种新方法以增强LLM在数学推理任务中的能力。为弥补这一差距，我们的方法引入了问题复述策略，旨在通过多样化数学问题的语言表达形式以提升泛化能力。同时，采用专门设计的训练目标来引导模型学习过程，重点增强其对数学概念和推理流程的理解。我们在四个数据集上使用不同LLM进行实验，证明了该方法在提升LLM数学推理任务性能方面的有效性。我们的研究结果凸显了该方法对推动大型语言模型发展的重要意义，以及对需要数学推理能力的实际应用场景的潜在影响。