用多步代码生成解决表格问答，准确率达70.5%
MRT at SemEval-2025 Task 8: Maximizing Recovery from Tables with Multiple Steps
- 分步生成代码：理解表结构→转为自然语言指令→转为代码→执行并处理错误
- 在子任务1上取得70.5%准确率，依赖优化提示词与开源LLM
- 适合需要高精度表格推理的开发者或研究者参考
本文介绍我们应对SemEval 2025任务8：表格数据上的问答挑战的方案。策略基于大模型生成Python代码，与表格交互以获取答案。流程包含多个步骤：理解表格内容、将操作转化为自然语言步骤、将步骤翻译为代码、执行代码并处理潜在错误或异常。各步骤使用开源LLM，并针对每个任务设计精细化提示词。该方法在子任务1上取得了70.5%的得分。
原文摘要 · Abstract (English)
In this paper we expose our approach to solve the \textit{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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