{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T21:06:13Z","timestamp":1775595973930,"version":"3.50.1"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"name":"National Science Foundation","award":["IIS-2238431"],"award-info":[{"award-number":["IIS-2238431"]}]},{"name":"National Science Foundation","award":["IIS-2335881"],"award-info":[{"award-number":["IIS-2335881"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2026,4,2]]},"abstract":"<jats:p>Large language models (LLMs) have achieved strong performance on natural language to SQL (NL2SQL) tasks, but their practical effectiveness depends on tuning a complex pipeline of interacting components. Real-world deployments must navigate a critical trade-off between execution accuracy and monetary cost, a factor that has been largely overlooked by prior work focused primarily on maximizing accuracy. Navigating this trade-off is non-trivial: the ideal configuration of components (e.g., LLM, prompting strategy, schema linking) is not only interdependent but also highly sensitive to the target database schema. This creates a challenging, schema-aware configuration tuning problem that lacks a systematic solution.<\/jats:p>\n                  <jats:p>We present PRISM, a framework that systematically identifies high-accuracy, cost-efficient NL2SQL configurations tailored to each schema. Adopting an optimize-then-deploy strategy, PRISM first uses cost-aware Bayesian Optimization in an offline phase to efficiently explore the configuration space and curate a pool of high-performing pipelines. In an online phase, it deploys these configurations either as a single, cost-effective candidate or as an ensemble to maximize accuracy. Experiments on the BIRD benchmark demonstrate that PRISM achieves 69.48% execution accuracy in the single-candidate setting, improving accuracy by 2.34% over the strongest baseline while reducing cost by 92%. In the ensemble setting, PRISM boosts accuracy further to 74.9%.<\/jats:p>","DOI":"10.1145\/3786679","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:54:13Z","timestamp":1775584453000},"page":"1-27","source":"Crossref","is-referenced-by-count":0,"title":["PRISM: Navigating Cost\u2013Accuracy Trade-offs for NL2SQL"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2750-5052","authenticated-orcid":false,"given":"Gaurav Tarlok","family":"Kakkar","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3947-2782","authenticated-orcid":false,"given":"Yeounoh","family":"Chung","sequence":"additional","affiliation":[{"name":"Google, Mountain View, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4418-4724","authenticated-orcid":false,"given":"Fatma","family":"\u00d6zcan","sequence":"additional","affiliation":[{"name":"Google, Mountain View, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5243-5944","authenticated-orcid":false,"given":"Steve","family":"Mussmann","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7706-6978","authenticated-orcid":false,"given":"Joy","family":"Arulraj","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue! arXiv preprint arXiv:2405.11706","author":"Allemang Dean","year":"2024","unstructured":"Dean Allemang and Juan Sequeda. 2024. 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