{"id":"W4416017187","doi":"10.1145/3746252.3761626","title":"Datasets for Supervised Adversarial Attacks on Neural Rankers","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo; University of Toronto","funders":"","keywords":"Adversarial system; Artificial neural network; Key (lock); Feature (linguistics); Supervised learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00281593,0.001832634,0.0008552953,0.002464478,0.001106327,0.001238802,0.002423106,0.003275272,0.01318227],"category_scores_gemma":[0.01257056,0.0005518046,0.001571188,0.00197182,0.001015495,0.001644879,0.001650923,0.002782661,0.009353761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001409746,"about_ca_system_score_gemma":0.001579898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005387963,"about_ca_topic_score_gemma":0.01241889,"domain_scores_codex":[0.9971611,0.0007013907,0.000237897,0.0004509384,0.001098854,0.0003498984],"domain_scores_gemma":[0.9935789,0.001725774,0.000359562,0.002946951,0.00115995,0.0002288859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001291018,0.001436013,0.004244678,0.00152816,0.0003548393,0.0003185386,0.0001056239,0.07043313,0.007590618,0.01248999,0.8110431,0.08916431],"study_design_scores_gemma":[0.00286456,0.001496111,0.02246207,0.0006317135,0.0002759052,0.002161088,0.0004903354,0.3540159,0.05548039,0.04282077,0.5169336,0.0003675046],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1307822,0.00395773,0.06356341,0.003882166,0.001737828,0.00201191,0.7418276,0.02159479,0.03064244],"genre_scores_gemma":[0.1565899,0.001067615,0.04866387,0.0007411222,0.0001893778,0.001361977,0.7784559,0.0009787829,0.01195145],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01318227,"threshold_uncertainty_score":0.04409903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193130906810358,"score_gpt":0.3210918387249055,"score_spread":0.299160529656802,"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."}}