@inproceedings{zhou-etal-2025-texts,
    title = "Texts or Images? A Fine-grained Analysis on the Effectiveness of Input Representations and Models for Table Question Answering",
    author = "Zhou, Wei  and
      Mesgar, Mohsen  and
      Adel, Heike  and
      Friedrich, Annemarie",
    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/corrections-2025-08/2025.findings-acl.117/",
    doi = "10.18653/v1/2025.findings-acl.117",
    pages = "2307--2318",
    ISBN = "979-8-89176-256-5",
    abstract = "In table question answering (TQA), tables are encoded as either texts or images. Prior work suggests that passing images of tables to multi-modal large language models (MLLMs) performs comparably to using textual input with large language models (LLMs). However, the lack of controlled setups limits fine-grained distinctions between these approaches. In this paper, we conduct the first controlled study on the effectiveness of several combinations of table representations and model types from two perspectives: question complexity and table size. We build a new benchmark based on existing TQA datasets. In a systematic analysis of seven pairs of MLLMs and LLMs, we find that the best combination of table representation and model varies across setups. We propose FRES, a method selecting table representations dynamically, and observe a 10{\%} average performance improvement compared to using both representations indiscriminately."
}