CLUES decomposes semantic uncertainty into separate ambiguity and instability scores for clinical Text-to-SQL, with instability via Schur complement, outperforming Kernel Language Entropy on failure prediction while enabling diagnostic triage.
Biomedsql: Text-to-sql for scientific reasoning on biomedical knowledge bases
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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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Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Study
CLUES decomposes semantic uncertainty into separate ambiguity and instability scores for clinical Text-to-SQL, with instability via Schur complement, outperforming Kernel Language Entropy on failure prediction while enabling diagnostic triage.