CHESS deploys four LLM agents to retrieve information, prune schemas, generate refined SQL candidates, and validate via unit tests, reporting up to 71.10% accuracy on BIRD with 83% fewer calls than leading proprietary baselines.
Evaluating cross-domain text-to-sql models and benchmarks
2 Pith papers cite this work. Polarity classification is still indexing.
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M3 uses LLMs to translate natural language into SQL for the MIMIC-IV database, achieving 93-94% accuracy on benchmark questions with support for local privacy-preserving deployment.
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M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis
M3 uses LLMs to translate natural language into SQL for the MIMIC-IV database, achieving 93-94% accuracy on benchmark questions with support for local privacy-preserving deployment.