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Ambiguity-aware NL2SQL generation framework produces realistic, complex queries that boost model performance on challenging real-world benchmarks, outperforming existing synthetic data.
A framework for evaluating data-science agents on live, continuously updated data using executable ground truth and format-agnostic factoid scoring, which achieves a 29% improvement in the Matthews Correlation Coefficient and a 16% reduction in token consumption per test case.