Sphinteract: Resolving Ambiguities in NL2SQL Through User Interaction

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Abstract

Translating natural language questions into SQL queries (NL2SQL) is a challenging task of great practical importance. Prior work has extensively studied how to address NL2SQL using Large Language Models (LLMs) with solutions ranging from careful prompt engineering, to fine-tuning existing LLMs, or even training custom models. However, a remaining challenging problem in NL2SQL is the inherent ambiguity in the natural language questions asked by users. In this paper, we introduce Sphinteract, a framework designed to assist LLMs in generating high-quality SQL answers that accurately reflect the user intent. Our key insight to resolve ambiguity is to take into account minimal user feedback interactively. We introduce the Summarize, Review, Ask (SRA) paradigm, which guides LLMs in identifying ambiguities in NL2SQL tasks and generates targeted questions for the user to answer. We propose three different methods of how to process user feedback and generate SQL queries based on user input. Our experiments on the challenging KaggleDBQA and BIRD benchmarks demonstrate that by means of asking clarification questions to the user, LLMs can efficiently incorporate the feedback, resulting in accuracy improvements of up to 42%.