![]() ![]() Offer insights into generating text-to-efficient-SQLs that are beneficial to Besides, we also provide an efficiency analysis to ![]() ChatGPT, only achieves 40.08% in executionĪccuracy, which is still far from the human result of 92.96%, proving thatĬhallenges still stand. Furthermore, even the mostĮffective text-to-SQL models, i.e. ![]() ![]() Generating accurate text-to-SQLs for big databases. TheĮxperimental results demonstrate the significance of database values in Must feature database value comprehension in addition to semantic parsing. To solve these problems, text-to-SQL models Highlights the new challenges of dirty database contents, external knowledgeīetween NL questions and database contents, and SQL efficiency, particularly in Of 33.4 GB, spanning 37 professional domains. To mitigate this gap, we presentīird, a big benchmark for large-scale database grounded in text-to-SQL tasks,Ĭontaining 12,751 pairs of text-to-SQL data and 95 databases with a total size However, most of the prevalent benchmarks, i.e., Spider, and WikiSQL, focus onĭatabase schema with few rows of database contents leaving the gap betweenĪcademic study and real-world applications. Particular, Codex and ChatGPT have shown impressive results in this task. Into executable SQLs, has gained increasing attention in recent years. Download a PDF of the paper titled Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs, by Jinyang Li and 17 other authors Download PDF Abstract: Text-to-SQL parsing, which aims at converting natural language instructions ![]()
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