@inproceedings{bujnowski-etal-2025-samsung,
title = "{S}amsung Research {P}oland at {S}em{E}val-2025 Task 8: {LLM} ensemble methods for {QA} over tabular data",
author = "Bujnowski, Pawel and
Dryjanski, Tomasz and
Goltz, Christian and
Swiderski, Bartosz and
Paszkiewicz, Natalia and
Kuzma, Bartlomiej and
Rutkowski, Jacek and
Stepka, Jakub and
Dudek, Milosz and
Siemiatkowski, Wojciech and
Plichta, Weronika and
Paziewski, Bart{\l}omiej and
Grabowski, Maciej and
Beksa, Katarzyna and
Bordzicka, Zuzanna and
Ostrowski, Filip and
Sochacki, Grzegorz",
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-1.163/",
pages = "1223--1232",
ISBN = "979-8-89176-273-2",
abstract = "Question answering using Large Language Models has gained significant popularity inboth everyday communication and at the workplace. However, certain tasks, such as querying tables, still pose challenges for commercial and open-source chatbots powered by advanceddeep learning models. Addressing these challenges requires specialized approaches.During the SemEval-2025 Task 8 competition focused on tabular data, our solution achieved86.21{\%} accuracy and took 2nd place out of 100 teams. In this paper we present ten methodsthat significantly improve the baseline solution. Our code is available as open-source."
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%0 Conference Proceedings
%T Samsung Research Poland at SemEval-2025 Task 8: LLM ensemble methods for QA over tabular data
%A Bujnowski, Pawel
%A Dryjanski, Tomasz
%A Goltz, Christian
%A Swiderski, Bartosz
%A Paszkiewicz, Natalia
%A Kuzma, Bartlomiej
%A Rutkowski, Jacek
%A Stepka, Jakub
%A Dudek, Milosz
%A Siemiatkowski, Wojciech
%A Plichta, Weronika
%A Paziewski, Bartłomiej
%A Grabowski, Maciej
%A Beksa, Katarzyna
%A Bordzicka, Zuzanna
%A Ostrowski, Filip
%A Sochacki, Grzegorz
%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 bujnowski-etal-2025-samsung
%X Question answering using Large Language Models has gained significant popularity inboth everyday communication and at the workplace. However, certain tasks, such as querying tables, still pose challenges for commercial and open-source chatbots powered by advanceddeep learning models. Addressing these challenges requires specialized approaches.During the SemEval-2025 Task 8 competition focused on tabular data, our solution achieved86.21% accuracy and took 2nd place out of 100 teams. In this paper we present ten methodsthat significantly improve the baseline solution. Our code is available as open-source.
%U https://aclanthology.org/2025.semeval-1.163/
%P 1223-1232
Markdown (Informal)
[Samsung Research Poland at SemEval-2025 Task 8: LLM ensemble methods for QA over tabular data](https://aclanthology.org/2025.semeval-1.163/) (Bujnowski et al., SemEval 2025)
ACL
- Pawel Bujnowski, Tomasz Dryjanski, Christian Goltz, Bartosz Swiderski, Natalia Paszkiewicz, Bartlomiej Kuzma, Jacek Rutkowski, Jakub Stepka, Milosz Dudek, Wojciech Siemiatkowski, Weronika Plichta, Bartłomiej Paziewski, Maciej Grabowski, Katarzyna Beksa, Zuzanna Bordzicka, Filip Ostrowski, and Grzegorz Sochacki. 2025. Samsung Research Poland at SemEval-2025 Task 8: LLM ensemble methods for QA over tabular data. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 1223–1232, Vienna, Austria. Association for Computational Linguistics.