cs.AI updates on arXiv.org 07月18日 12:14
MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps
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本文提出了一种基于大型语言模型(LLM)的西班牙语表格问答解决方案,通过Python代码生成和表内容处理,实现高效问答,达到85%的准确率。

arXiv:2507.12981v1 Announce Type: cross Abstract: This paper presents our approach for the IberLEF 2025 Task PRESTA: Preguntas y Respuestas sobre Tablas en Espa\~nol (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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LLM 表格问答 西班牙语 Python代码生成 Semeval 2025
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