Ambiguity in user queries is a major cause of failure in text-to-SQL systems, often leading to silent deviations from intent. The ARCS paper proposes structured disambiguation, replacing inefficient free-form dialogue with explicit, constrained interactions to resolve these issues. To support this paradigm, the authors introduce the Ambiguity Resolution Corpus for SQL, the first benchmark featuring naturally occurring unconstrained ambiguities.
- Structured disambiguation offers a more efficient alternative to conversational clarification for resolving query ambiguity.
- ARCS benchmark provides the first dataset of naturally occurring, unconstrained ambiguities in text-to-SQL tasks.
- Constrained interactions align better with real-world user workflows than free-form dialogue.
- This approach targets silent errors that cause system outputs to deviate from true user intent.