2025
ACL
ACL 2025
Samsung Research Poland at SemEval-2025 Task 8: LLM ensemble methods for QA over tabular data
Abstract
AbstractQuestion 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.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
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
,
Grzegorz Sochacki
Topics
Artificial Intelligence > Core AI > Foundation Models
Natural Language Processing > Applications > Question Answering
Natural Language Processing > Resources & Methods > Large Language Models
Machine Learning > Learning Types > Ensemble Learning
Deep Learning > Models > Large Language Models
Machine Learning > Core Methods > Ensemble Learning