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