{"id":"W4400529018","doi":"10.1145/3626772.3657979","title":"Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?","year":2024,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Generalization; Encoder; Query expansion; Artificial intelligence; Data mining; Information retrieval; Mathematics; Operating system","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":[0.005729945,0.001388661,0.001511278,0.001685816,0.000602235,0.001395407,0.001984592,0.001432656,0.002749792],"category_scores_gemma":[0.02002782,0.0005023729,0.001012182,0.001081132,0.001113221,0.004814153,0.002130158,0.001595663,0.001801376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009446222,"about_ca_system_score_gemma":0.001195056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003853434,"about_ca_topic_score_gemma":0.005543896,"domain_scores_codex":[0.9967197,0.001141818,0.0002279747,0.0008068872,0.0007973432,0.0003062865],"domain_scores_gemma":[0.9910109,0.004065307,0.0005807958,0.002529089,0.001512634,0.0003013187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001512877,0.001206094,0.02110938,0.0006307685,0.0004340936,0.0005006965,0.0009199693,0.1420751,0.06441545,0.0161817,0.01438876,0.7366251],"study_design_scores_gemma":[0.0001195587,0.0009242656,0.005010172,0.00004515304,0.0002102219,0.000613336,0.000254567,0.94175,0.03089374,0.01438119,0.005703833,0.00009392026],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4116887,0.00360797,0.5628011,0.001628696,0.000225984,0.0003253551,0.0007919192,0.009175562,0.009754688],"genre_scores_gemma":[0.8883022,0.0004870219,0.1030992,0.0006532982,0.0001506372,0.00009774631,0.0010876,0.0003490867,0.00577326],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005729945,"threshold_uncertainty_score":0.03030318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224747830565552,"score_gpt":0.2762828888245987,"score_spread":0.2640354105189432,"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."}}