No Universal Prompt: Unifying Reasoning through Adaptive Prompting for Temporal Table Reasoning

Temporal Table Reasoning is a critical challenge for Large Language Models (LLMs), requiring effective prompting techniques to extract relevant insights. Despite existence of multiple prompting methods, their impact on table reasoning remains largely unexplored. Furthermore, the performance of these models varies drastically across different table and context structures, making it difficult to determine an optimal approach. This work investigates multiple prompting technique across diverse table types to determine optimal approaches for different scenarios. We find that performance varies based on entity type, table structure, requirement of additional context and question complexity, with NO single method consistently outperforming others. To mitigate these challenges, we introduce SEAR, an adaptive prompting framework inspired by human reasoning that dynamically adjusts based on context characteristics and integrates a structured reasoning. Our results demonstrate that SEAR achieves superior performance across all table types compared to other baseline prompting techniques. Additionally, we explore the impact of table structure refactoring, finding that a unified representation enhances model's reasoning.