BenchPress: A Human-in-the-Loop Annotation System for Rapid Text-to-SQL Benchmark Curation
Summary: BenchPress: a human-in-the-loop RAG+LLM pipeline that generates candidate natural-language utterances for SQL-log queries, letting experts select/edit to rapidly curate domain-specific text-to-SQL benchmarks. Reduces annotation cost and improves evaluation robustness for private enterprise workloads. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Fabian Wenz (Massachusetts Institute of Technology; Technical University of Munich)
- 2. Omar Bouattour (Massachusetts Institute of Technology; Technical University of Munich)
- 3. Devin Yang (Massachusetts Institute of Technology)
- 4. Justin Choi (Massachusetts Institute of Technology)
- 5. Cecil Gregg (Massachusetts Institute of Technology)
- 6. Nesime Tatbul (Intel; Massachusetts Institute of Technology)
- 7. Cagatay Demiralp (Amazon; Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{wenz_cidr26,
address = {Amsterdam, Netherlands},
series = {{CIDR} '26},
title = {{BenchPress: A Human-in-the-Loop Annotation System for Rapid Text-to-SQL Benchmark Curation}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Wenz, Fabian and Bouattour, Omar and Yang, Devin and Choi, Justin and Gregg, Cecil and Tatbul, Nesime and Demiralp, Cagatay},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,211 | GenEdit: Compounding Operators and Continuous Improvement to Tackle Text-to-SQL in the Enterprise | 2025 | CIDR | 5.9425753e-05 |
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