针对时间表格推理，提出自适应提示框架，提升模型泛化能力。
No Universal Prompt: Unifying Reasoning through Adaptive Prompting for Temporal Table Reasoning
- 设计自适应提示框架SEAR，模拟人类推理动态调整策略。
- 在所有表格类型上表现优于基线方法，平均提升12.3%准确率。
- 适合需要跨结构表格推理的复杂任务场景，如金融分析、医疗记录。
时间表格推理是大语言模型的一项关键挑战，需有效推理以提取相关洞察。尽管存在多种提示方法，其对表格推理的影响仍缺乏系统研究。模型性能在不同表格和上下文结构间差异显著，难以确定最优方案。本工作在多种表格类型上评估多种提示技术，发现性能受实体类型、表格结构、额外上下文需求及问题复杂度影响，不存在单一方法始终领先。为此，我们提出SEAR——一种受人类推理启发的自适应提示框架，能根据上下文动态调整并整合结构化推理。结果表明，SEAR在所有表格类型上均优于基线提示方法。此外，我们探索了表格结构重构的影响，发现统一表示可增强模型推理能力。
原文摘要 · Abstract (English)
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.
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