Large Language Models (LLMs) have demonstrated impressive capabilities across a range of natural language processing tasks. In particular, improvements in reasoning abilities and the expansion of context windows have opened new avenues for leveraging these powerful models. NL2SQL is challenging in that the natural language question is inherently ambiguous, while the SQL generation requires a precise understanding of complex data schema and semantics. One approach to this semantic ambiguous problem is to provide more and sufficient contextual information. In this work, we explore the performance and the latency trade-offs of the extended context window (a.k.a., long context) offered by Google's state-of-the-art LLM (\textit{gemini-1.5-pro}). We study the impact of various contextual information, including column example values, question and SQL query pairs, user-provided hints, SQL documentation, and schema. To the best of our knowledge, this is the first work to study how the extended context window and extra contextual information can help NL2SQL generation with respect to both accuracy and latency cost. We show that long context LLMs are robust and do not get lost in the extended contextual information. Additionally, our long-context NL2SQL pipeline based on Google's \textit{gemini-pro-1.5} achieve a strong performance with 67.41\% on BIRD benchmark (dev) without finetuning and expensive self-consistency based techniques.

本研究探讨了大型语言模型（LLMs）在NL2SQL任务中的应用，尤其是在处理自然语言问题的模糊性和SQL生成的复杂性方面。通过分析延长上下文窗口对性能和延迟的权衡，首次说明了如何通过额外的上下文信息提升NL2SQL的准确性和效率。研究结果表明，基于长上下文的LLMs在处理大量上下文信息时表现稳定，且无需微调即可在BIRD基准测试中实现67.41%的良好性能。

长上下文是你所需的一切吗？利用大语言模型的扩展上下文进行NL2SQL