MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps
This work addresses question-answering over Spanish tables, but it is incremental as it builds on prior MRT implementations for similar tasks.
The paper tackled the problem of answering questions from Spanish tables by implementing a multi-step Python code generation approach using LLMs, achieving an accuracy of 85% in the PRESTA task.
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.