SOMA-SQL: New Method Resolves Ambiguity in Natural Language Database Queries
Researchers have developed SOMA-SQL, a technique that automatically resolves ambiguities when translating natural language questions into SQL database queries. The method uses synthetic query logs and targeted probing to disambiguate user intent, schema interpretation, and SQL generation without requiring human intervention. This addresses a major limitation of current natural language-to-SQL systems, which often fail when questions are underspecified or database schemas are complex.
SOMA-SQL is a new approach to improving natural language-to-SQL (NL2SQL) systems, which translate user questions into executable database queries. The core problem it addresses is ambiguity at multiple levels: unclear user intent, complex database schemas, and competing model interpretations. Rather than relying on human clarification or treating ambiguity solely as a schema representation issue, SOMA-SQL autonomously resolves ambiguity through two main mechanisms: constructing synthetic query logs to ground schema interpretation and guide SQL generation, and executing targeted probing queries informed by a structured ambiguity taxonomy. Testing on six public benchmarks showed the method improved execution accuracy by an average of 13.0% over existing state-of-the-art approaches, with improvements up to 16.7% on questions containing ambiguities. The technique generalizes across unseen schemas and query distributions without human-in-the-loop intervention.
What's missing
The paper does not discuss computational costs or inference latency compared to baseline methods, nor does it address potential failure modes when synthetic query logs are unavailable or unrepresentative of real-world usage patterns.
What different sources said
- arXiv cs.CLCenter
SOMA-SQL: Resolving Multi-Source Ambiguity in NL-to-SQL via Synthetic Log and Execution Probing
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