Table as Thought：探索 LLM 推理中的结构化思维
Table as Thought: Exploring Structured Thoughts in LLM Reasoning
AI总结：
本文提出 Table as Thought 框架，用表格结构组织 LLM 的思维步骤、约束与上下文，并通过迭代填充和自验证提升规划与数学推理能力。
摘要：
大型语言模型的推理能力受益于那些组织其思维过程的方法，例如 chain-of-thought prompting（思维链提示），它采用顺序结构逐步引导推理过程。然而，现有方法主要关注组织思维的顺序，而单个思维步骤内部的结构仍未得到充分探索。为弥补这一空白，我们提出了 Table as Thought，这是一个受有关人类思维的认知神经科学理论启发的框架。Table as Thought 在表格模式中组织推理，其中行表示顺序的思维步骤，列捕获关键约束和上下文信息以增强推理。推理过程会迭代填充表格，直到自验证确保完整性和正确性。实验表明，Table as Thought 在规划任务中表现出色，并且与非结构化思维基线相比，在提升 LLM 数学推理性能方面展现出强大潜力。本工作对完善 LLM 内部的思维表示进行了新颖探索，为推理和 AI 认知的进步铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
