This paper describes our participation in SemEval 2025 Task 8, focused on Tabular Question Answering. We developed a zero-shot pipeline that leverages an Large Language Model to generate functional code capable of extracting the relevant information from tabular data based on an input question. Our approach consists of a modular pipeline where the main code generator module is supported by additional components that identify the most relevant columns and analyze their data types to improve extraction accuracy. In the event that the generated code fails, an iterative refinement process is triggered, incorporating the error feedback into a new generation prompt to enhance robustness. Our results show that zero-shot code generation is a valid approach for Tabular QA, achieving rank 33 of 53 in the test phase despite the lack of task-specific fine-tuning.

翻译：本文介绍了我们参与 SemEval 2025 任务 8（专注于表格问答）的工作。我们开发了一个零样本流水线，利用大型语言模型生成能够根据输入问题从表格数据中提取相关信息的可执行代码。我们的方法采用模块化流水线设计，其中主代码生成器模块由多个辅助组件支持，这些组件负责识别最相关的列并分析其数据类型，以提高信息提取的准确性。若生成的代码执行失败，系统将触发迭代优化过程，将错误反馈整合到新的生成提示中以增强鲁棒性。我们的结果表明，零样本代码生成是解决表格问答问题的有效方法，尽管未进行任务特定的微调，仍在测试阶段取得了 53 支队伍中排名第 33 的成绩。