{"id":"W4416374244","doi":"10.48550/arxiv.2510.04919","title":"Do LLMs Align with My Task? Evaluating Text-to-SQL via Dataset Alignment","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada","keywords":"Generalization; SQL; Training set; Selection (genetic algorithm); Natural language; Data modeling","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01294722,0.00145529,0.001026322,0.001259384,0.0008451396,0.002509829,0.002245034,0.002246,0.002731036],"category_scores_gemma":[0.05249271,0.0005593264,0.001071253,0.001424408,0.001116477,0.005144831,0.002550043,0.002794139,0.003322373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001362927,"about_ca_system_score_gemma":0.002106388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008397946,"about_ca_topic_score_gemma":0.01084709,"domain_scores_codex":[0.9914526,0.003826531,0.0006239386,0.002565286,0.001106335,0.0004253104],"domain_scores_gemma":[0.980176,0.0112897,0.0009915861,0.004969341,0.001718485,0.0008548907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003922578,0.002124875,0.08893962,0.001528235,0.001234732,0.0005779642,0.001407271,0.2784728,0.03347977,0.003697821,0.07913024,0.5054841],"study_design_scores_gemma":[0.000386259,0.001436067,0.01880198,0.0001050155,0.000192944,0.0003253017,0.001055786,0.9319315,0.02418115,0.009699041,0.01178663,0.00009824644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8244011,0.003844165,0.1084885,0.003214557,0.0007491185,0.0006028183,0.008672487,0.04218001,0.007847031],"genre_scores_gemma":[0.8989899,0.0003873384,0.06915487,0.001196472,0.0001277035,0.0002767813,0.02604783,0.001688527,0.002130646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01294722,"threshold_uncertainty_score":0.06847227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04374433755076232,"score_gpt":0.3442167162795454,"score_spread":0.3004723787287831,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}