{"id":"W7108328114","doi":"10.1145/3767695.3769517","title":"Adversarial Attacks against Neural Ranking Models via In-Context Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Adversarial system; Ranking (information retrieval); Generalization; Set (abstract data type); Rank (graph theory); Scalability; Artificial neural network","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":[0.002138716,0.0012035,0.0008697569,0.000439181,0.0005183865,0.0009031288,0.001362188,0.001159287,0.002065712],"category_scores_gemma":[0.01067118,0.0003632142,0.000604903,0.0003097266,0.001542986,0.001942381,0.002263337,0.002588458,0.0008271927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008748569,"about_ca_system_score_gemma":0.0007826225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001454351,"about_ca_topic_score_gemma":0.002096433,"domain_scores_codex":[0.9982637,0.0007693752,0.00007110955,0.00029962,0.0004232078,0.0001730301],"domain_scores_gemma":[0.9951133,0.003241268,0.0003929924,0.0008518382,0.0002788275,0.0001218083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002274129,0.000101498,0.001328171,0.0001474118,0.00008838538,0.000204978,0.0001315063,0.8654107,0.01179635,0.03726196,0.004630791,0.07867075],"study_design_scores_gemma":[0.000009325326,0.00006283927,0.00009380462,0.000007655594,0.000008389996,0.00005392127,0.00001005215,0.9826171,0.003432312,0.01293683,0.0007574018,0.00001036789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05345977,0.0006461499,0.9377335,0.0007332448,0.0001242361,0.0001145215,0.0001618757,0.002695635,0.004331158],"genre_scores_gemma":[0.9118101,0.0002723237,0.08191174,0.0005803341,0.0001039763,0.0001425782,0.000231309,0.000226908,0.004720626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002138716,"threshold_uncertainty_score":0.01131076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01465561121597099,"score_gpt":0.2690068626495586,"score_spread":0.2543512514335876,"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."}}