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This paper investigates the performance of Large Language Models (LLMs) andTool-augmented LLMs in tackling complex mathematical reasoning tasks. Weintroduce IMP-TIP: Improving Math Reasoning with Tool-augmented InterleafPrompting, a framework that combines the strengths of both LLMs andTool-augmented LLMs. IMP-TIP follows the ``From Good to Great" concept,collecting multiple potential solutions from both LLMs and their Tool-Augmentedcounterparts for the same math problem, and then selecting or re-generating themost accurate answer after cross-checking these solutions via tool-augmentedinterleaf prompting. The framework incorporates two key aspects: self-promptand tool-augmented interleaf prompting (TIP). The former allows LLMs toautonomously refine and improve an initial prompt related to tool usage, whilethe latter enables LLMs to derive the final answer by dynamically analyzing theproblem, cross-checking potential solutions, and revising previous reasoninghints in an interleaved manner. Experimental analysis shows that IMP-TIPachieves enhanced mathematical capabilities and outperforms traditional LLMsand tool-augmented LLMs in accuracy and reasoning diversity on math reasoningtasks. For instance, IMP-TIP can improve Tool-augmented ChatGPT on GSM8K-Hardfrom 56.0% to 65.2%.

From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting