André Bergmann Lisboa

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2025

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QleverAnswering-PUCRS at SemEval-2025 Task 8: Exploring LLM agents, code generation and correction for Table Question Answering
André Bergmann Lisboa | Lucas Cardoso Azevedo | Lucas Rafael Costella Pessutto
Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)

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.
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