@inproceedings{kang-etal-2026-ease,
    title = "{EASE}: Entity-Aware Sub-table Generation for Real-world Multi-table {QA}",
    author = "Kang, Myunghoon  and
      Jung, Dahyun  and
      Son, Suhyune  and
      Koo, Seonmin  and
      Chun, Changwoo  and
      Rim, Daniel  and
      Kwon, Haeyoung  and
      Hur, Yuna  and
      Lim, Heuiseok",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.10/",
    pages = "277--302",
    ISBN = "979-8-89176-390-6",
    abstract = "Recent advancements in table-based question answering (table QA) have been driven by the development of table-specific reasoning strategies for leveraging large language models. Previous works employ sub-table-based reasoning, which involves matching query-relevant table values and aggregating them into sub-tables for precise reasoning. However, these approaches are limited to scenarios with query-relevant single tables, failing to handle real-world table QA settings that involve noisy multi-table sets. To address the challenges of real-world table QA, we propose **EASE**: **E**ntity-**A**ware **S**ub-table Generation for R**E**al-world Multi-table QA framework. Given a noisy multi-table set, EASE first extracts key entities from the question to construct a sub-table schema. It then populates this schema by utilizing a selected set of column values from the noisy multi-table set, thereby facilitating efficient and effective sub-table-based reasoning. We introduce a Noisy Multi-table QA dataset and conduct extensive experiments to evaluate EASE{'}s effectiveness on real-world table QA. Our results demonstrate that EASE effectively filters out irrelevant information while incorporating pertinent table values, leading to efficient and effective performance on real-world table QA. Our dataset can be found https://github.com/Metalchaos8527/ease{\_}noisy{\_}multi-table{\_}qa.git"
}