@inproceedings{hormazabal-lagos-etal-2025-mrt,
title = "{MRT} at {S}em{E}val-2025 Task 8: Maximizing Recovery from Tables with Multiple Steps",
author = "Hormaz{\'a}bal Lagos, Maximiliano and
Bueno S{\'a}ez, {\'A}lvaro and
Cerezo - Costas, H{\'e}ctor and
Alonso Doval, Pedro and
Alcalde Vesteiro, Jorge",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.68/",
pages = "487--493",
ISBN = "979-8-89176-273-2",
abstract = "In this paper we expose our approach to solve the SemEval 2025 Task 8: Question-Answering over Tabular Data challenge. Our strategy leverages Python code generation with LLMs to interact with the table and get the answer to the questions. The process is composed of multiple steps: understanding the content of the table, generating natural language instructions in the form of steps to follow in order to get the answer, 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 task (step). With this approach, we achieved a score of 70.50{\%} for subtask 1."
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<abstract>In this paper we expose our approach to solve the SemEval 2025 Task 8: Question-Answering over Tabular Data challenge. Our strategy leverages Python code generation with LLMs to interact with the table and get the answer to the questions. The process is composed of multiple steps: understanding the content of the table, generating natural language instructions in the form of steps to follow in order to get the answer, 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 task (step). With this approach, we achieved a score of 70.50% for subtask 1.</abstract>
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%0 Conference Proceedings
%T MRT at SemEval-2025 Task 8: Maximizing Recovery from Tables with Multiple Steps
%A Hormazábal Lagos, Maximiliano
%A Bueno Sáez, Álvaro
%A Cerezo - Costas, Héctor
%A Alonso Doval, Pedro
%A Alcalde Vesteiro, Jorge
%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 hormazabal-lagos-etal-2025-mrt
%X In this paper we expose our approach to solve the SemEval 2025 Task 8: Question-Answering over Tabular Data challenge. Our strategy leverages Python code generation with LLMs to interact with the table and get the answer to the questions. The process is composed of multiple steps: understanding the content of the table, generating natural language instructions in the form of steps to follow in order to get the answer, 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 task (step). With this approach, we achieved a score of 70.50% for subtask 1.
%U https://aclanthology.org/2025.semeval-1.68/
%P 487-493
Markdown (Informal)
[MRT at SemEval-2025 Task 8: Maximizing Recovery from Tables with Multiple Steps](https://aclanthology.org/2025.semeval-1.68/) (Hormazábal Lagos et al., SemEval 2025)
ACL