MAC-SQL + GPT-4

Reported on 4 benchmarks across 2 tasks · 1 paper · 4 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing4 results

Semantic ParsingonBIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation)
Execution Accuracy % (Dev)· 2023-12-18
57.56
best: 74.32 (DSAIR + GPT-4o)
SOTA
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL arXiv:2312.11242
Semantic ParsingonBIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation)
Execution Accuracy % (Test)· 2023-12-18
59.59
best: 75.63 (XiYan-SQL)
SOTA
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL arXiv:2312.11242
Text-To-SQLonBIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation)
Execution Accuracy % (Dev)· 2023-12-18
57.56
best: 74.32 (DSAIR + GPT-4o)
SOTA
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL arXiv:2312.11242
Text-To-SQLonBIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation)
Execution Accuracy % (Test)· 2023-12-18
59.59
best: 75.63 (XiYan-SQL)
SOTA
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL arXiv:2312.11242