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    <abstract>We present a practical framework for detecting errors in LLM-generated SQL by estimating uncertainty at the level of individual nodes in the query’s abstract syntax tree (AST). Our approach proceeds in two stages. First, we introduce a semantically aware labeling algorithm that, given a generated SQL and a gold reference, assigns node-level correctness without over-penalizing structural containers or alias variation. Second, we represent each node with a rich set of schema-aware and lexical features - capturing identifier validity, alias resolution, type compatibility, ambiguity in scope, and typo signals - and train a supervised classifier to predict per-node error probabilities. We interpret these probabilities as calibrated uncertainty, enabling fine-grained diagnostics that pinpoint exactly where a query is likely to be wrong. Across multiple databases and datasets, our method substantially outperforms token log-probabilities: average AUC improves by +27.44% while maintaining robustness under cross-database evaluation. Beyond serving as an accuracy signal, node-level uncertainty supports targeted repair, human-in-the-loop review, and downstream selective execution. Together, these results establish node-centric, semantically grounded uncertainty estimation as a strong and interpretable alternative to aggregate sequence-level confidence measures.</abstract>
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%0 Conference Proceedings
%T Node-Level Uncertainty Estimation in LLM-Generated SQL
%A Hasson, Hilaf
%A Guo, Ruocheng
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F hasson-guo-2026-node
%X We present a practical framework for detecting errors in LLM-generated SQL by estimating uncertainty at the level of individual nodes in the query’s abstract syntax tree (AST). Our approach proceeds in two stages. First, we introduce a semantically aware labeling algorithm that, given a generated SQL and a gold reference, assigns node-level correctness without over-penalizing structural containers or alias variation. Second, we represent each node with a rich set of schema-aware and lexical features - capturing identifier validity, alias resolution, type compatibility, ambiguity in scope, and typo signals - and train a supervised classifier to predict per-node error probabilities. We interpret these probabilities as calibrated uncertainty, enabling fine-grained diagnostics that pinpoint exactly where a query is likely to be wrong. Across multiple databases and datasets, our method substantially outperforms token log-probabilities: average AUC improves by +27.44% while maintaining robustness under cross-database evaluation. Beyond serving as an accuracy signal, node-level uncertainty supports targeted repair, human-in-the-loop review, and downstream selective execution. Together, these results establish node-centric, semantically grounded uncertainty estimation as a strong and interpretable alternative to aggregate sequence-level confidence measures.
%R 10.63317/3i2i8xy5e7hs
%U https://aclanthology.org/2026.lrec-1.168/
%U https://doi.org/10.63317/3i2i8xy5e7hs
%P 2145-2153

Markdown (Informal)

[Node-Level Uncertainty Estimation in LLM-Generated SQL](https://aclanthology.org/2026.lrec-1.168/) (Hasson & Guo, LREC 2026)

• Node-Level Uncertainty Estimation in LLM-Generated SQL (Hasson & Guo, LREC 2026)

ACL

• Hilaf Hasson and Ruocheng Guo. 2026. Node-Level Uncertainty Estimation in LLM-Generated SQL. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 2145–2153, Palma de Mallorca, Spain. ELRA Language Resource Association.