@inproceedings{bystronski-etal-2026-factual,
    title = "Factual State Discovery Benchmark: Evaluating Fact Elicitation in {P}olish Tax Law",
    author = "Bystro{\'n}ski, Mateusz  and
      Tagowski, Kamil  and
      Janiak, Denis  and
      Farganus, Julia  and
      Augustyniak, Lukasz  and
      Kajdanowicz, Monika  and
      Kajdanowicz, Tomasz Jan",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-srw.114/",
    pages = "1262--1279",
    ISBN = "979-8-89176-393-7",
    abstract = "Before a tax authority can issue a ruling, it must receive a complete description of the taxpayer{'}s situation{---}yet no benchmark measures whether language models can systematically elicit all relevant facts through dialogue.We introduce \textbf{FSDBench} (\textbf{Factual State Discovery Benchmark}), in which a discovery agent questions a simulated taxpayer grounded in a real tax document.The dataset comprises 500 narratives from official Polish tax interpretations, decomposed into 32 874 atomic facts with validated supported precision (97.6{\%}), atomicity (93.8{\%}), and sentence coverage (96.0{\%}).Experiments with four models show that even the best system recovers only 77{\%} of facts on easy samples and under 49{\%} on hard samples after 50 turns.These findings establish conversational fact elicitation as a challenging open problem requiring retrieval-augmented and adaptive questioning strategies."
}