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Text-to-SQL parsing, which aims at converting natural language instructionsinto executable SQLs, has gained increasing attention in recent years. Inparticular, Codex and ChatGPT have shown impressive results in this task.However, most of the prevalent benchmarks, i.e., Spider, and WikiSQL, focus ondatabase schema with few rows of database contents leaving the gap betweenacademic study and real-world applications. To mitigate this gap, we presentBird, a big benchmark for large-scale database grounded in text-to-SQL tasks,containing 12,751 pairs of text-to-SQL data and 95 databases with a total sizeof 33.4 GB, spanning 37 professional domains. Our emphasis on database valueshighlights the new challenges of dirty database contents, external knowledgebetween NL questions and database contents, and SQL efficiency, particularly inthe context of massive databases. To solve these problems, text-to-SQL modelsmust feature database value comprehension in addition to semantic parsing. Theexperimental results demonstrate the significance of database values ingenerating accurate text-to-SQLs for big databases. Furthermore, even the mosteffective text-to-SQL models, i.e. ChatGPT, only achieves 40.08% in executionaccuracy, which is still far from the human result of 92.96%, proving thatchallenges still stand. Besides, we also provide an efficiency analysis tooffer insights into generating text-to-efficient-SQLs that are beneficial toindustries. We believe that BIRD will contribute to advancing real-worldapplications of text-to-SQL research. The leaderboard and source code areavailable: https://bird-bench.github.io/.

Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs