Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQL

TL;DR

This paper investigates how extended context windows in large language models can improve NL2SQL task performance by providing more contextual information, balancing accuracy gains with latency costs, and demonstrating robustness without fine-tuning.

Contribution

It is the first study to analyze the effects of extended context windows and additional contextual information on NL2SQL accuracy and latency using Google's Gemini-1.5-Pro model.

Findings

Long context LLMs are robust and do not lose relevant information.

Extended context improves NL2SQL accuracy without fine-tuning.

The approach achieves strong benchmark performance with manageable latency.

Abstract

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 strong performances on various benchmark datasets without finetuning and expensive self-consistency based techniques.