@inproceedings{oses-grijalba-etal-2025-semeval,
title = "{S}em{E}val-2025 Task 8: Question Answering over Tabular Data",
author = "Os{\'e}s Grijalba, Jorge and
Ure{\~n} - L{\'o}pez, L. Alfonso and
Mart{\'i}nez C{\'a}mara, Eugenio and
Camacho - Collados, Jose",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.324/",
pages = "2512--2522",
ISBN = "979-8-89176-273-2",
abstract = "We introduce the findings and results of SemEval-2025 Task 8: Question Answering over Tabular Data. We featured two subtasks, DataBench and DataBench Lite. DataBench consists on question answering over tabular data, and DataBench Lite small comprising small datasets that might be easier to manage by current models by for example fitting them into a prompt. The task was open for any approach, but their answer has to conform to a required typing format. In this paper we present the task, analyze a number of system submissions and discuss the results. The results show how approaches leveraging LLMs dominated the task, with larger models exhibiting a considerably superior performance compared to small models."
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<abstract>We introduce the findings and results of SemEval-2025 Task 8: Question Answering over Tabular Data. We featured two subtasks, DataBench and DataBench Lite. DataBench consists on question answering over tabular data, and DataBench Lite small comprising small datasets that might be easier to manage by current models by for example fitting them into a prompt. The task was open for any approach, but their answer has to conform to a required typing format. In this paper we present the task, analyze a number of system submissions and discuss the results. The results show how approaches leveraging LLMs dominated the task, with larger models exhibiting a considerably superior performance compared to small models.</abstract>
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%0 Conference Proceedings
%T SemEval-2025 Task 8: Question Answering over Tabular Data
%A Osés Grijalba, Jorge
%A Ureñ - López, L. Alfonso
%A Martínez Cámara, Eugenio
%A Camacho - Collados, Jose
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F oses-grijalba-etal-2025-semeval
%X We introduce the findings and results of SemEval-2025 Task 8: Question Answering over Tabular Data. We featured two subtasks, DataBench and DataBench Lite. DataBench consists on question answering over tabular data, and DataBench Lite small comprising small datasets that might be easier to manage by current models by for example fitting them into a prompt. The task was open for any approach, but their answer has to conform to a required typing format. In this paper we present the task, analyze a number of system submissions and discuss the results. The results show how approaches leveraging LLMs dominated the task, with larger models exhibiting a considerably superior performance compared to small models.
%U https://aclanthology.org/2025.semeval-1.324/
%P 2512-2522
Markdown (Informal)
[SemEval-2025 Task 8: Question Answering over Tabular Data](https://aclanthology.org/2025.semeval-1.324/) (Osés Grijalba et al., SemEval 2025)
ACL
- Jorge Osés Grijalba, L. Alfonso Ureñ - López, Eugenio Martínez Cámara, and Jose Camacho - Collados. 2025. SemEval-2025 Task 8: Question Answering over Tabular Data. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2512–2522, Vienna, Austria. Association for Computational Linguistics.