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

2025-01-21

AI Summary

NL2SQL faces challenges due to natural language ambiguity and SQL’s structural complexity. To address this, we propose a zero-shot method leveraging long-context augmentation, capitalizing on Gemini-1.5-Pro’s extended context window to systematically integrate heterogeneous contextual signals—including database schema, column-value examples, SQL syntax documentation, question-answer pairs, and user prompts—thereby mitigating semantic ambiguity. We present the first empirical analysis of the joint impact of long-context length on both execution accuracy and inference latency, demonstrating that substantial gains in zero-shot generalization can be achieved without fine-tuning or computationally expensive techniques like self-consistency. Our approach achieves 67.41% execution accuracy on the BIRD (dev) benchmark, establishing a new paradigm for efficient, lightweight NL2SQL systems.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Learning to Search
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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 ( extit{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 extit{gemini-pro-1.5} achieve a strong performance with 67.41% on BIRD benchmark (dev) without finetuning and expensive self-consistency based techniques.

Problem

• Large Language Models
• Natural Language to SQL (NL2SQL) Task
• Polysemy and Complexity Resolution

Innovation

• Large Language Models
• NL2SQL Task
• Background Information