Year: 2025
Type: conference-paper
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.
Authors: Zhenjie Sun, Naihao Deng, Haofei Yu, Jiaxuan You
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412944133