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Research questionHow can natural-language interfaces generate reliable SQL and GraphQL without runtime LLM inference?Natural-language database interfaces commonly target only SQL and rely on an LLM for every query, yet syntactically valid outputs may still express the wrong meaning. Supporting multiple query languages while keeping inference lightweight also requires a way to signal possible semantic errors at runtime.
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Latest papersRecent research connected to this question, newest first.text2ql: Multi-Target Natural Language Querying via a Language-Agnostic Intermediate RepresentationThe source describes text2ql, an open-source Python framework with a language-agnostic intermediate representation and renderers for SQL and GraphQL. It reports deterministic and LLM-backed modes plus runtime confidence scores, but its evidence is limited to 50-query random samples from each of the Spider and BIRD benchmarks; full-set evaluation is planned.research paper · Sep 2, 2026
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How can natural-language interfaces generate reliable SQL over nested, graph-like enterprise schemas at acceptable cost?How can governed language-model analytics preserve expressive queries while producing replayable, evidence-backed answers?How can Text-to-SQL systems learn from database execution when annotated question-SQL pairs are unavailable?How can recurring natural-language text functions become reusable local programs without runtime remote inference?