Papers › KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers
KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers
Chia-Hsuan Lee, Oleksandr Polozov, Matthew Richardson
The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains. Recently, large-scale datasets such as Spider and WikiSQL facilitated novel modeling techniques for text-to-SQL parsing, improving zero-shot generalization to unseen databases. In this work, we examine the challenges that still prevent these techniques from practical deployment. First, we present KaggleDBQA, a new cross-domain evaluation dataset of real Web databases, with domain-specific data types, original formatting, and unrestricted questions. Second, we re-examine the choice of evaluation tasks for text-to-SQL parsers as applied in real-life settings. Finally, we augment our in-domain evaluation task with database documentation, a naturally occurring source of implicit domain knowledge. We show that KaggleDBQA presents a challenge to state-of-the-art zero-shot parsers but a more realistic evaluation setting and creative use of associated database documentation boosts their accuracy by over 13.2%, doubling their performance.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text-To-SQL | KaggleDBQA | RAT-SQL | Exact Match (EM) | 26.77 | #1 of 2 | Archive leaderboard | report |
| Text-To-SQL | KaggleDBQA | Edit-SQL | Exact Match (EM) | 11.73 | #2 of 2 | Archive leaderboard | report |
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