TACO: A Benchmark for Open-Domain Text-to-SQL with Ambiguous and Cross-Database Queries
Summary: TACO benchmarks open-domain text-to-SQL beyond standard closed-schema settings, targeting ambiguous questions, unspecified databases, and cross-database queries. It combines 1.5K real smart-city examples with 13K synthesized open-data queries, plus a TACO-SQL baseline revealing a large gap to human SQL. (summarized by gpt-5.4-mini on Apr 12 2026)
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Authors
- 1. Chao Deng
- 2. Ju Fan
- 3. Yuyu Luo
- 4. Qinliang Xue
- 5. Meihao Fan
- 6. Yuxin Zhang
- 7. Min Zhang
- 8. Xiaofeng Jia
- 9. Jing Zhang
- 10. Xiaoyong Du
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 973 | Natural language to SQL: Where are we today? | 2020 | VLDB | 0.0001488435 |
| 998 | CodeS: Towards Building Open-source Language Models for Text-to-SQL | 2024 | SIGMOD | 0.00014726344 |
| 2,948 | Few-shot Text-to-SQL Translation using Structure and Content Prompt Learning | 2023 | SIGMOD | 7.8301799e-05 |
| 4,908 | Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL | 2024 | VLDB | 5.835596e-05 |
| 5,448 | SNAILS: Schema Naming Assessments for Improved LLM-Based SQL Inference | 2025 | SIGMOD | 5.4980173e-05 |
| 10,690 | AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework | 2025 | VLDB | 4.1905499e-05 |
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