T-REX: Table - Refute or Entail eXplainer
Abstract
Verifying textual claims against structured tabular data is a critical yet challenging task in Natural Language Processing with broad real-world impact. While recent advances in Large Language Models (LLMs) have enabled significant progress in table fact-checking, current solutions remain inaccessible to non-experts. We introduce T-REX (Table – Refute or Entail eXplainer), the first live, interactive tool for claim verification over multimodal, multilingual tables using state-of-the-art instruction-tuned reasoning LLMs. Designed for accuracy and transparency, T-REX empowers non-experts by providing access to advanced fact-checking technology. The system is openly available online. Online Demo: https://t-rex.r2.enst.fr Demo (video): https://www.youtube.com/watch?v=HHIxVCOT8X0 Github: https://github.com/TimLukaHorstmann/T-REX
Cite
Text
Horstmann et al. "T-REX: Table - Refute or Entail eXplainer." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025. doi:10.1007/978-3-032-06129-4_33Markdown
[Horstmann et al. "T-REX: Table - Refute or Entail eXplainer." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025.](https://mlanthology.org/ecmlpkdd/2025/horstmann2025ecmlpkdd-trex/) doi:10.1007/978-3-032-06129-4_33BibTeX
@inproceedings{horstmann2025ecmlpkdd-trex,
title = {{T-REX: Table - Refute or Entail eXplainer}},
author = {Horstmann, Tim Luka and Geisenberger, Baptiste and Alam, Mehwish},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2025},
pages = {470-474},
doi = {10.1007/978-3-032-06129-4_33},
url = {https://mlanthology.org/ecmlpkdd/2025/horstmann2025ecmlpkdd-trex/}
}