@inproceedings{rajgaria-etal-2025-universal,
    title = "No Universal Prompt: Unifying Reasoning through Adaptive Prompting for Temporal Table Reasoning.",
    author = "Rajgaria, Abhishek  and
      Dixit, Kushagra  and
      Vyas, Mayank  and
      Kalalbandi, Harshavardhan  and
      Roth, Dan  and
      Gupta, Vivek",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.150/",
    pages = "2800--2821",
    ISBN = "979-8-89176-298-5",
    abstract = "Temporal Table Reasoning poses a significant 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. SEAR{\_}Unified, its cost-efficient variant. We also demonstrate that optional table refactoring (preprocessing) enhances both approaches when tables lack structural consistency. Our results demonstrate that SEAR prompts achieve superior performance across all table types compared to baseline prompting techniques"
}