SOURCE-LINKED INTELLIGENCE

Evaluating LLMs on Conversational Text-to-SQL under Chain Ambiguity and Intent Drift

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

Recent advances in large language models (LLMs) have established conversational text-to-SQL as a practical interface between users and databases, often involving multiple turns of clarification and revision. However, existing benchmarks primarily evaluate execution accuracy, leaving the unfolding and shifting of user intent across turns largely uncovered. To address this, we introduce TIDE-Bench, a benchmark for conversational text-to-SQL under chain ambiguity and intent drift evaluation, targeting two recurring patterns: chain ambiguity, where an underspecified question triggers layered clari

Read original source ↗ Open in workspace

recordType

paper

region

Global