PARTAB is a framework that improves large language model reasoning on tables by constructing a structured evidence interface. It represents query‑relevant evidence as semantically coherent, row‑linked table regions and performs hierarchical selection over column groups and row‑level partitions before composing the evidence for answer generation. Evaluations on multiple table reasoning benchmarks show that PARTAB consistently outperforms full‑table prompting and recent methods, achieving strong performance on WikiTableQuestions and TabFact while remaining competitive on numerical reasoning tasks.
By Md Mahadi Hasan Nahid, Davood Rafiei
Large Language Models (LLMs) have shown strong capabilities in table reasoning, but their effectiveness degrades as tables grow in size and complexity due to irrelevant context and difficulty localizi...
arXiv:2506. 18421v3 Announce Type: replace-cross Abstract: The majority of data in businesses and industries is stored in tables, databases, and data warehouses.
By Ce Li, Xiaofan Liu, Zhiyan Song, Ce Chi, Boshen Shi, Chen Zhao, Guanguang Chang, Zhendong Wang, Kexin Yang, Xing Wang, Chao Deng, Junlan Feng
arXiv:2605. 20254v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown promising results on NLP tasks, however, their performance on tabular data still needs research attention, because Table Question-Answering (TQA) requires precise cell retrieval and multi-step structured reasoning.
By Amritansh Maurya, Navjot Singh, Mohammed Javed, Omar Moured
arXiv:2607. 11207v1 Announce Type: cross Abstract: Table-based reasoning with large language models (LLMs), which requires reasoning based on natural language questions and structured tabular data, has gained widespread attention.
By Pei Guo, Enjie Liu, Yunzhi Tan, Mochi Gao, Jianxin Zhang, Ruichao Zhong, Juntao Li, Bo Hu, Zang Li
The paper introduces Table Graph Reasoner (TabGR), a model that represents tables as an Attributed Table Graph (ATG) to preserve row-column-cell structure and enable graph-based reasoning without task-specific training. It also proposes a Question-Guided Personalized PageRank (QG-PPR) mechanism to rerank tabular data and address the lost-in-the-middle issue. Experiments on multiple table reasoning benchmarks show that TabGR outperforms state-of-the-art models by up to 9.7% in accuracy.
By Yuxiang Wang, Junhao Gan, Shengxiang Gao, Shenghao Ye, Zhengyi Yang, Jianzhong Qi