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    <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.</abstract>
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%0 Conference Proceedings
%T Tables as Thought: Exploring Structured Thoughts in LLM Reasoning
%A Sun, Zhenjie
%A Deng, Naihao
%A Yu, Haofei
%A You, Jiaxuan
%Y Chang, Shuaichen
%Y Hulsebos, Madelon
%Y Liu, Qian
%Y Chen, Wenhu
%Y Sun, Huan
%S Proceedings of the 4th Table Representation Learning Workshop
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-268-8
%F sun-etal-2025-tables
%X 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.
%R 10.18653/v1/2025.trl-1.3
%U https://aclanthology.org/2025.trl-1.3/
%U https://doi.org/10.18653/v1/2025.trl-1.3
%P 19-33

Markdown (Informal)

[Tables as Thought: Exploring Structured Thoughts in LLM Reasoning](https://aclanthology.org/2025.trl-1.3/) (Sun et al., TRL 2025)

• Tables as Thought: Exploring Structured Thoughts in LLM Reasoning (Sun et al., TRL 2025)

ACL

• Zhenjie Sun, Naihao Deng, Haofei Yu, and Jiaxuan You. 2025. Tables as Thought: Exploring Structured Thoughts in LLM Reasoning. In Proceedings of the 4th Table Representation Learning Workshop, pages 19–33, Vienna, Austria. Association for Computational Linguistics.