Table as Thought: Exploring Structured Thoughts in LLM Reasoning

• URL: http://arxiv.org/abs/2501.02152v1
• Date: Sat, 04 Jan 2025 00:58:06 GMT
• Title: Table as Thought: Exploring Structured Thoughts in LLM Reasoning
• Authors: Zhenjie Sun, Naihao Deng, Haofei Yu, Jiaxuan You,
• Abstract summary: Large language models' reasoning abilities benefit from methods that organize their thought processes.<n>Existing approaches focus primarily on organizing the sequence of thoughts, leaving structure in individual thought steps underexplored.<n>We propose Table as Thought, a framework inspired by cognitive neuroscience theories on human thought.
• Score: 14.901120719649315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models' reasoning abilities benefit from methods that organize their thought processes, such as chain-of-thought prompting, which employs a sequential structure to guide the reasoning process step-by-step. However, existing approaches focus primarily on organizing the sequence of thoughts, leaving structure in individual thought steps underexplored. To address this gap, we propose Table as Thought, a framework inspired by cognitive neuroscience theories on human thought. Table as Thought organizes reasoning within a tabular schema, where rows represent sequential thought steps and columns capture critical constraints and contextual information to enhance reasoning. The reasoning process iteratively populates the table until self-verification ensures completeness and correctness. Our experiments show that Table as Thought excels in planning tasks and demonstrates a strong potential for enhancing LLM performance in mathematical reasoning compared to unstructured thought baselines. This work provides a novel exploration of refining thought representation within LLMs, paving the way for advancements in reasoning and AI cognition.

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