@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://aclanthology.org/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"
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<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</abstract>
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%0 Conference Proceedings
%T No Universal Prompt: Unifying Reasoning through Adaptive Prompting for Temporal Table Reasoning.
%A Rajgaria, Abhishek
%A Dixit, Kushagra
%A Vyas, Mayank
%A Kalalbandi, Harshavardhan
%A Roth, Dan
%A Gupta, Vivek
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S 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
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F rajgaria-etal-2025-universal
%X 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
%U https://aclanthology.org/2025.ijcnlp-long.150/
%P 2800-2821
Markdown (Informal)
[No Universal Prompt: Unifying Reasoning through Adaptive Prompting for Temporal Table Reasoning.](https://aclanthology.org/2025.ijcnlp-long.150/) (Rajgaria et al., IJCNLP-AACL 2025)
ACL
- Abhishek Rajgaria, Kushagra Dixit, Mayank Vyas, Harshavardhan Kalalbandi, Dan Roth, and Vivek Gupta. 2025. No Universal Prompt: Unifying Reasoning through Adaptive Prompting for Temporal Table Reasoning.. In 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, pages 2800–2821, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.