Data Generation

Recent advances in text-to-SQL systems have been driven by larger models and improved datasets, yet progress is still limited by the scarcity of high-quality training data. Manual data creation is expensive, and existing synthetic methods trade off reliability and scalability. Template-based approaches ensure correct SQL but require schema-specific templates, while LLM-based generation scales easily but lacks quality and correctness guarantees. We introduce RingSQL, a hybrid data generation framework that combines schema-independent query templates with LLM-based paraphrasing of natural-language questions. This approach preserves SQL correctness across diverse schemas while providing broad linguistic variety. In our experiments, we find that models trained using data produced by RingSQL achieve an average gain in accuracy of +2.3% across six text-to-SQL benchmarks when compared to models trained on other synthetic data.

Jan 7, 2026

The U.S. court system is the nation’s arbiter of justice, tasked with the responsibility of ensuring equal protection under the law. But hurdles to information access obscure the inner workings of the system, preventing stakeholders – from legal scholars to journalists and members of the public – from understanding the state of justice in America at scale. There is an ongoing data access argument here: U.S. court records are public data and should be freely available. But open data arguments represent a half-measure; what we really need is open information. This distinction marks the difference between downloading a zip file containing a quarter-million case dockets and getting the real-time answer to a question like “Are pro se parties more or less likely to receive fee waivers?” To help bridge that gap, we introduce a novel platform and user experience that provides users with the tools necessary to explore data and drive analysis via natural language statements. Our approach leverages an ontology configuration that adds domain-relevant data semantics to database schemas to provide support for user guidance and for search and analysis without user-entered code or SQL. The system is embodied in a “natural-language notebook” user experience, and we apply this approach to the space of case docket data from the U.S. federal court system. Additionally, we provide detail on the collection, ingestion and processing of the dockets themselves, including early experiments in the use of language modeling for docket entry classification with an initial focus on motions.

Jun 21, 2021