Rethinking Text-to-SQL: Dynamic Multi-turn SQL Interaction for Real-world Database Exploration
This addresses a critical limitation for users in finance and business analytics who need iterative database exploration, though it is incremental as it builds on existing Text-to-SQL work with a new benchmark.
The paper tackles the problem of Text-to-SQL systems failing in real-world interactive scenarios where user intents evolve over multiple turns, by introducing DySQL-Bench, a benchmark for evaluating dynamic multi-turn SQL interaction, which shows that even GPT-4o achieves only 58.34% overall accuracy and 23.81% on Pass@5, highlighting its difficulty.
Recent advances in Text-to-SQL have achieved strong results in static, single-turn tasks, where models generate SQL queries from natural language questions. However, these systems fall short in real-world interactive scenarios, where user intents evolve and queries must be refined over multiple turns. In applications such as finance and business analytics, users iteratively adjust query constraints or dimensions based on intermediate results. To evaluate such dynamic capabilities, we introduce DySQL-Bench, a benchmark assessing model performance under evolving user interactions. Unlike previous manually curated datasets, DySQL-Bench is built through an automated two-stage pipeline of task synthesis and verification. Structured tree representations derived from raw database tables guide LLM-based task generation, followed by interaction-oriented filtering and expert validation. Human evaluation confirms 100% correctness of the synthesized data. We further propose a multi-turn evaluation framework simulating realistic interactions among an LLM-simulated user, the model under test, and an executable database. The model must adapt its reasoning and SQL generation as user intents change. DySQL-Bench covers 13 domains across BIRD and Spider 2 databases, totaling 1,072 tasks. Even GPT-4o attains only 58.34% overall accuracy and 23.81% on the Pass@5 metric, underscoring the benchmark's difficulty. All code and data are released at https://github.com/Aurora-slz/Real-World-SQL-Bench .