{"id":"W4385573576","doi":"10.18653/v1/2022.findings-emnlp.146","title":"MatRank: Text Re-ranking by Latent Preference Matrix","year":2022,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Ranking (information retrieval); Computer science; Learning to rank; Information retrieval; Rank (graph theory); Preference; Artificial intelligence; Precision and recall; Macro; Key (lock); Latent variable; Machine learning; Natural language processing; Statistics; Mathematics","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":[0.001460147,0.002195128,0.001781917,0.004019801,0.0007274034,0.001501317,0.001819078,0.001005882,0.004555183],"category_scores_gemma":[0.007038306,0.0004897224,0.00112651,0.003652556,0.0005415972,0.003644584,0.001296489,0.001511147,0.005189179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007560239,"about_ca_system_score_gemma":0.001752745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006359854,"about_ca_topic_score_gemma":0.01865264,"domain_scores_codex":[0.9981665,0.0005523836,0.0001194952,0.0004035698,0.0006108081,0.0001473361],"domain_scores_gemma":[0.9972028,0.0009253318,0.0003224454,0.0007407722,0.0006531359,0.0001557051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005574694,0.0005706177,0.005152403,0.0008064361,0.0003375366,0.0002839374,0.0002188938,0.07365612,0.01840964,0.01032245,0.1196868,0.7699976],"study_design_scores_gemma":[0.000198174,0.0003064592,0.001548714,0.00003621892,0.00008032455,0.0004016752,0.0000955861,0.9485465,0.01093477,0.02171956,0.0160357,0.00009621074],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03742626,0.003264222,0.9182228,0.0007609179,0.0005437395,0.0006058033,0.006553104,0.02868254,0.003940694],"genre_scores_gemma":[0.3358349,0.002001219,0.6132399,0.0005699758,0.0008195903,0.0006802679,0.02670748,0.001370935,0.01877568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006359854,"threshold_uncertainty_score":0.01523864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03992445200966278,"score_gpt":0.2490979534830197,"score_spread":0.2091735014733569,"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."}}