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.

In this work we present an overview of our winning system for the R2VQ - Competence-based Multimodal Question Answering task, with the final exact match score of 92.53%.The task is structured as question-answer pairs, querying how well a system is capable of competence-based comprehension of recipes. We propose a hybrid of a rule-based system, Question Answering Transformer, and a neural classifier for N/A answers recognition. The rule-based system focuses on intent identification, data extraction and response generation.