A developer has achieved a 77.1% execution accuracy on the BIRD text-to-SQL benchmark using a single model call per question with Claude Opus 5.5. This approach, implemented via the schemagate system, involves the model selecting relevant tables and generating a single query, with a mechanism to fix errors if SQLite encounters them. The developer is submitting these results to the hidden test set to determine the system's performance against other leading methods that typically use multiple calls per question. The system also demonstrates strong performance on access-control benchmarks, achieving zero ungranted reads. AI
IMPACT Demonstrates efficient LLM querying for text-to-SQL tasks, potentially reducing computational costs for complex data analysis.
RANK_REASON The item details performance on a specific academic benchmark (BIRD text-to-SQL) using a particular LLM (Claude Opus 5.5), which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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