Two Surfaces of Ambiguity: Complementary Detection for Text-to-SQL

TraceSQL: Traceable Answerability Estimation for Reference-Free Text-to-SQL Verification

Results show that SQL verification can be performed with a lightweight learned model while retaining feature-level evidence for inspecting and diagnosing its predictions, and feature attribution shows that the model relies on both semantic grounding and deterministic SQL-structure signals.

ParSEval : Interactive Counterexample-driven Evaluation for Text-to-SQL

Bounded Semantic Planning and Deterministic Compilation for Reliable Enterprise Text-to-SQL

This semantic path compilation (SPC) system against direct DDL-to-SQL generation on the ACME insurance benchmark supports an end-to-end systems result, not a causal claim that compilation alone produced the gain, because SPC receives governed semantic artifacts that the DDL baseline does not.

BIRD-History: A Benchmark for History-Driven Text-to-SQL with Fine-Grained Knowledge Annotations

BIRD-History is introduced, a benchmark consisting of 1,393 tasks across 11 databases, designed to evaluate text-to-SQL systems'ability to ground underspecified natural language questions using historical SQL scripts, and a plug-in retriever that extracts five types of external knowledge from historical SQL scripts, then retrieves and reranks relevant fragments for query generation.

Developing and Benchmarking Verification Algorithms to Improve Text-to-SQL Generation

Large Language Models (LLMs) have achieved impressive performance in translating natural language queries into executable SQL. However, these systems remain prone to deceptive failures : generating syntactically valid SQL that executes but fails to capture the user's intent. In this work, we argue that further progress can be made by focusing specifically on verification. In this paper, we formalize Text-to-SQL verification as a standalone task: assessing whether a candidate SQL query semantically satisfies a natural language request, without access to ground-truth labels. We propose and compare two modular verification strategies: Round-Trip Critique , which reverse-translates SQL into natural language to detect semantic drift, and Synthetic Execution Consistency , which uses unit-test-like synthetic inputs to ground verification in execution results. Our evaluation shows that these methods provide a robust signal for identifying incorrect queries, successfully flagging 64% of errors from a state-of-the-art generator and outperforming standard error detectors. We demonstrate two critical applications: (1) auditing foundational benchmarks (Spider, BIRD and KaggleDBQA), revealing that over two-thirds of "generator failures" actually stem from flawed benchmark labels, and (2) enabling Selective Generation, where a system uses verification signals to abstain from answering when confidence is low. Our results show that this paradigm significantly improves the quality of deployed data interfaces by transforming silent failures into explicit abstentions that alert the user to the failure and give them a chance to correct it.

Benchmarking Text-to-SQL under Role-Based Access Control

A comprehensive text-to-SQL benchmarking framework with realistic RBAC constraints is presented, which features an LLM-assisted workflow that augments existing text-to-SQL benchmarks with plausible user roles and access policies, and contains evaluation metrics that identify RBAC-specific failure modes, and disentangle SQL utility from access-control compliance.