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    <abstract>Table Question Answering (TQA) is a challenging task that requires reasoning over structured data to extract accurate answers. This paper presents QleverAnswering-PUCRS, our submission to SemEval-2025 Task 8: DataBench, Question-Answering over Tabular Data. QleverAnswering-PUCRS is a modular multi-agent system that employs a structured approach to TQA. The approach revolves around breaking down the task into specialized agents, each dedicated to handling a specific aspect of the problem. Our system was evaluated on benchmark datasets and achieved competitive results, ranking mid-to-top positions in the SemEval-2025 competition. Despite these promising results, we identify areas for improvement, particularly in handling complex queries and nested data structures.</abstract>
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%0 Conference Proceedings
%T QleverAnswering-PUCRS at SemEval-2025 Task 8: Exploring LLM agents, code generation and correction for Table Question Answering
%A Bergmann Lisboa, André
%A Cardoso Azevedo, Lucas
%A Costella Pessutto, Lucas Rafael
%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 bergmann-lisboa-etal-2025-qleveranswering
%X Table Question Answering (TQA) is a challenging task that requires reasoning over structured data to extract accurate answers. This paper presents QleverAnswering-PUCRS, our submission to SemEval-2025 Task 8: DataBench, Question-Answering over Tabular Data. QleverAnswering-PUCRS is a modular multi-agent system that employs a structured approach to TQA. The approach revolves around breaking down the task into specialized agents, each dedicated to handling a specific aspect of the problem. Our system was evaluated on benchmark datasets and achieved competitive results, ranking mid-to-top positions in the SemEval-2025 competition. Despite these promising results, we identify areas for improvement, particularly in handling complex queries and nested data structures.
%U https://aclanthology.org/2025.semeval-1.179/
%P 1342-1350

Markdown (Informal)

[QleverAnswering-PUCRS at SemEval-2025 Task 8: Exploring LLM agents, code generation and correction for Table Question Answering](https://aclanthology.org/2025.semeval-1.179/) (Bergmann Lisboa et al., SemEval 2025)

• QleverAnswering-PUCRS at SemEval-2025 Task 8: Exploring LLM agents, code generation and correction for Table Question Answering (Bergmann Lisboa et al., SemEval 2025)

ACL

• André Bergmann Lisboa, Lucas Cardoso Azevedo, and Lucas Rafael Costella Pessutto. 2025. QleverAnswering-PUCRS at SemEval-2025 Task 8: Exploring LLM agents, code generation and correction for Table Question Answering. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 1342–1350, Vienna, Austria. Association for Computational Linguistics.