@inproceedings{zhang-etal-2025-scitat,
    title = "{SCITAT}: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types",
    author = "Zhang, Xuanliang  and
      Wang, Dingzirui  and
      Wang, Baoxin  and
      Dou, Longxu  and
      Lu, Xinyuan  and
      Xu, Keyan  and
      Wu, Dayong  and
      Zhu, Qingfu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.199/",
    pages = "3859--3881",
    ISBN = "979-8-89176-256-5",
    abstract = "Scientific question answering (SQA) is an important task aimed at answering questions based on papers. However, current SQA datasets have limited reasoning types and neglect the relevance between tables and text, creating a significant gap with real scenarios. To address these challenges, we propose a QA benchmark for scientific tables and text with diverse reasoning types (SCITAT). To cover more reasoning types, we summarize various reasoning types from real-world questions. To reason on both tables and text, we require the questions to incorporate tables and text as much as possible. Based on SCITAT, we propose a baseline (CAR), which combines various reasoning methods to address different reasoning types and process tables and text at the same time. CAR brings average improvements of 4.1{\%} over other baselines on SCITAT, validating its effectiveness. Error analysis reveals the challenges of SCITAT, such as complex numerical calculations and domain knowledge."
}