没有通用的提示：通过自适应提示统一时序表格推理
No Universal Prompt: Unifying Reasoning through Adaptive Prompting for Temporal Table Reasoning
AI总结：
本研究针对LLM在时序表格推理中缺乏通用最优提示方法的问题，提出了受人类推理启发的自适应提示框架SEAR，动态适应上下文并整合结构化推理，在各类表格上超越基线方法，并发现统一表格表示能增强模型推理。
摘要：
时序表格推理是大型语言模型（LLMs）面临的一项关键挑战，需要有效的推理来提取相关见解。尽管存在多种提示方法，但它们对表格推理的影响在很大程度上仍未被探索。此外，模型性能在不同的表格和上下文结构之间差异巨大，使得确定一种最优方法变得困难。本研究在多种表格类型上调查了多种提示技术，发现性能取决于实体类型、表格结构、额外上下文需求和问题复杂度等因素，没有任何单一方法能始终优于其他方法。为了解决这一问题，我们引入了SEAR，这是一种受人类推理启发的自适应提示框架，能够动态适应上下文并整合结构化推理。我们的结果表明，与基线提示技术相比，SEAR在所有表格类型上均取得了更优的性能。此外，我们探讨了表格结构重构的影响，发现统一的表示能增强模型推理。
英文摘要：
Temporal Table Reasoning is a critical challenge for Large Language Models (LLMs), requiring effective reasoning to extract relevant insights. Despite existence of multiple prompting methods, their impact on table reasoning remains largely unexplored. Furthermore, model performance varies drastically across different table and context structures, making it difficult to determine an optimal approach. This work investigates multiple prompting technique on diverse table types to determine that performance depends on factors such as entity type, table structure, requirement of additional context and question complexity, with "NO" single method consistently outperforming others. To address this, we introduce SEAR, an adaptive prompting framework inspired by human reasoning that dynamically adjusts to context and integrates structured reasoning. Our results demonstrate that SEAR achieves superior performance across all table types compared to baseline prompting techniques. Additionally, we explore the impact of table structure refactoring, finding that a unified representation enhances model reasoning.
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。
