MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps

Maximiliano Hormazábal Lagos, Álvaro Bueno Sáez, Héctor Cerezo-Costas, Pedro Alonso Doval, Jorge Alcalde Vesteiro
7/17/2025
cs.CLcs.AI

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Abstract

This paper presents our approach for the IberLEF 2025 Task PRESTA: Preguntas y Respuestas sobre Tablas en Español (Questions and Answers about Tables in Spanish). Our solution obtains answers to the questions by implementing Python code generation with LLMs that is used to filter and process the table. This solution evolves from the MRT implementation for the Semeval 2025 related task. The process consists of multiple steps: analyzing and understanding the content of the table, selecting the useful columns, generating instructions in natural language, translating these instructions to code, running it, and handling potential errors or exceptions. These steps use open-source LLMs and fine-grained optimized prompts for each step. With this approach, we achieved an accuracy score of 85\% in the task.

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Cite this paper

@article{lagos2025iberlef,
  title  = {MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps},
  author = {Maximiliano Hormazábal Lagos and Álvaro Bueno Sáez and Héctor Cerezo-Costas and Pedro Alonso Doval and Jorge Alcalde Vesteiro},
  year   = {2025},
  eprint = {2507.12981},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.12981v1}
}

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