SOMA-SQL: New Method Resolves Ambiguity in Natural Language Database Queries
Researchers have proposed SOMA-SQL, a system designed to automatically resolve ambiguity when translating natural language questions into SQL database queries. The system uses synthetic query logs and targeted probing queries to disambiguate user intent without requiring human intervention. The work addresses a persistent bottleneck in deploying natural language database interfaces at scale in real-world settings.
A team of researchers has introduced SOMA-SQL, a framework aimed at tackling multi-source ambiguity in natural language to SQL (NL2SQL) translation — a longstanding challenge in building practical database interfaces. The system identifies three central failure modes: ambiguous user questions, large or unclear database schemas, and inconsistent model interpretations. To address these, SOMA-SQL constructs synthetic query logs to anchor schema interpretation and guide candidate SQL generation, then executes targeted probing queries informed by a structured ambiguity taxonomy to gather disambiguation evidence. This evidence is used to select and repair the final SQL output without any human-in-the-loop involvement. Experiments across six public benchmarks reportedly show an average improvement of 13.0% in execution accuracy over state-of-the-art baselines, with gains reaching 16.7% on explicitly ambiguous questions. The paper is a preprint submitted to arXiv and has not yet undergone peer review.
What's missing
As a preprint, this work has not been peer-reviewed, and independent replication has not been reported. The paper does not detail computational costs or latency overhead introduced by the synthetic log construction and probing steps, which are relevant to real-world deployment feasibility. It is also unclear how the system performs on proprietary or highly domain-specific schemas not represented in the six public benchmarks.
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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