Text to SQL Benchmark

Text-to-SQL benchmarks evaluate the ability of natural language processing models to translate human-readable questions into executable SQL queries, aiming to improve the accessibility of databases for non-programmers. Current research focuses on developing more robust and realistic benchmarks that reflect the complexities of real-world data and queries, often employing large language models (LLMs) with techniques like chain-of-thought prompting, self-correction, and multi-agent collaboration to enhance accuracy and address issues like hallucinations and semantic errors. These advancements are crucial for building more reliable and practical text-to-SQL systems, impacting various fields by enabling easier data access and analysis for a wider range of users.

Papers