@inproceedings{grijalba-etal-2026-problem,
    title = "The Problem of Ambiguity in Table Question Answering",
    author = "Grijalba, Jorge Os{\'e}s  and
      Ure{\~n}a, L. Alfonso  and
      Mart{\'i}nez-C{\'a}mara, Eugenio  and
      Camacho-Collados, Jose",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.199/",
    pages = "3835--3848",
    ISBN = "979-8-89176-386-9",
    abstract = "Question Answering on Tabular Data (or Table Question Answering) has seen tremendous advances with the coming of new generation Large Language Models (LLMs). Despite this, significant challenges still remain to be solved if we are to develop robust enough approaches for general usage. One of these is ambiguity in question answering, which historically has not merited much attention due to the previously limited capabilities of LLMs. In this work, we outlay the main types of ambiguousness inherent to tabular data. Then, we discuss how they are influenced by the way our models interact with the information stored in the tables, and we test the capabilities of some LLMs in detecting them. This work provides an initial ground for a deeper discussion on how to approach ambiguity in Tabular Data in the age of LLMs."
}