Probing Ambiguity: Adversarial Intent Resolution for Robust Text-to-SQL

Key points

• Text-to-SQL systems typically assume a one-to-one mapping between natural-language queries and correct SQL translations.1
• In practice, however, many queries are inherently ambiguous, admitting multiple valid interpretations that yield distinct outputs.1
• On the AMBROSIA benchmark under schema-only context, a realistic production setting where exposing the full database is infeasible, AIR outperforms IntentRL, the state-of-the-art reinforcement learning model, across all metrics (e.g., F1 0.679 vs. 0.618).1
• On the AmbiQT benchmark, where ambiguity arises from schema design rather than linguistic structure, AIR achieves higher F1 under both context settings without any benchmark-specific adaptation.1

Sources (1)

• [1]Probing Ambiguity: Adversarial Intent Resolution for Robust Text-to-SQL
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 09:20 PM

  We introduce Adversarial Intent Resolution (AIR), a multi-agent framework that actively probes and resolves such ambiguity without any task-specific training.

  Text-to-SQL systems typically assume a one-to-one mapping between natural-language queries and correct SQL translations.

Extractive summary: sentences quoted from the sources.