The article explores the effectiveness of the THINK-AND-EXECUTE framework in improving algorithmic reasoning capabilities of large language models (LLMs). Compared to direct prompting and zero-shot Chain of Thought methods, THINK-AND-EXECUTE shows significant performance gains, indicating the importance of generating a structured plan. Task-level pseudocode prompts outperform instance-specific Python code in broader application, demonstrating their effectiveness across various reasoning tasks. Additionally, the logic derived from LLMs through this framework can be transferred to smaller language models, expanding the potential for algorithmic reasoning in diverse contexts.
In terms of algorithmic reasoning, our THINK-AND-EXECUTE framework significantly outperforms direct prompting and zero-shot CoT, highlighting the benefits of generating a planning step.
While zero-shot CoT acts as a strong baseline, our findings demonstrate that explicitly generating a plan enhances the reasoning capabilities of LLMs across various tasks.
Task-level pseudocode prompts were found to be more broadly beneficial for algorithmic reasoning tasks than instance-specific Python code, enhancing reasoning performance consistently.
The logic and reasoning capabilities discovered by the LLM can be transferred to smaller language models, indicating a broader applicability of the reasoning strategies.
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