SOMA-SQL: Resolving Multi-Source Ambiguity in NL-to-SQL via Synthetic Log and Execution Probing
arXiv:2606.11424v1 Announce Type: new Abstract: Natural language interfaces to databases aim to translate user questions into executable SQL, yet remain brittle in real-world settings where questions are underspecified and schemas are large and ambiguous. Ambiguity across user questions, database schemas, and model interpretations are central failure modes in NL2SQL, leading to misaligned intent, incorrect schema grounding, and erroneous SQL generation. Existing approaches rely on human clarification or treat ambiguity as a schema representation problem, but these do not scale nor resolve ambi
The proliferation of natural language interfaces to databases (NL2SQL) combined with increasing complexity in data schemas necessitates more robust methods for ambiguity resolution, which this paper directly addresses.
Improving NL2SQL systems to handle ambiguity is critical for advancing autonomous AI agents and making complex databases accessible to non-technical users, thereby expanding the utility of AI in enterprise and beyond.
This advancement makes NL2SQL systems more reliable and less prone to errors caused by ambiguous user queries or database schemas, moving closer to truly intelligent data interaction.
- · AI software developers
- · Enterprises with complex databases
- · Data analysts
- · SaaS providers leveraging NL2SQL
- · Legacy database interaction methods
- · Systems requiring extensive manual SQL crafting
More accurate and reliable natural language interactions with databases will become commonplace.
This improved reliability will accelerate the adoption and sophistication of autonomous AI agents interacting with data.
The enhanced data accessibility could lead to new business intelligence paradigms and a significant reduction in data-related workflow friction across industries.
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Read at arXiv cs.CL