Large Language Models (LLMs) have demonstrated remarkable performance in various NLP tasks, including semantic parsing, which trans lates natural language into formal code representations. However, the reverse process, translating code into natural language, termed semantic captioning, has received less attention. This task is becoming increasingly important as LLMs are integrated into platforms for code generation, security analysis, and educational purposes. In this paper, we focus on the captioning of SQL query (SQL2Text) to address the critical need for understanding and explaining SQL queries in an era where LLM-generated code poses potential security risks. We repurpose Text2SQL datasets for SQL2Text by introducing an iterative ICL prompt using GPT-4o to generate multiple additional utterances, which enhances the robustness of the datasets for the reverse task. We conduct our experiments using in-context learning (ICL) based on different sample selection methods, emphasizing smaller, more computationally efficient LLMs. Our findings demonstrate that leveraging the inherent graph properties of SQL for ICL sample selection significantly outperforms random selection by up to 39% on BLEU score and provides better results than alternative methods. Dataset and codes are published: \url{https://github.com/aliwister/ast-icl}.

本研究解决了语义描述在自然语言处理中的缺乏关注，尤其是在将SQL查询翻译为自然语言的过程。我们通过引入迭代的上下文学习提示，利用GPT-4o增强数据集的稳健性，从而提高了处理反向任务的效果。研究结果表明，利用SQL的图特性进行样本选择的有效性，使得模型在语义描述任务上的表现优于随机选择，BLEU得分提高了39%。

语义描述：基准数据集与图感知少样本上下文学习用于SQL转文本