{"id":"W4225362522","doi":"10.1007/978-3-030-99739-7_24","title":"How Different are Pre-trained Transformers for Text Ranking?","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Canadian Institute of Steel Construction","keywords":"Computer science; Transformer; Artificial intelligence; Ranking (information retrieval); Machine learning; Information retrieval; Relevance (law); Question answering; Encoder; Task (project management); Recall; Artificial neural network; Learning to rank; Precision and recall; Deep learning; Natural language processing; Deep neural networks","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":[0.008871355,0.001744929,0.001771419,0.002509979,0.0009191241,0.004699777,0.002640639,0.002916074,0.007967296],"category_scores_gemma":[0.04243112,0.0008516066,0.001539909,0.001911077,0.001131837,0.01668343,0.001690057,0.004396523,0.01155778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001374988,"about_ca_system_score_gemma":0.00266891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004799989,"about_ca_topic_score_gemma":0.009493167,"domain_scores_codex":[0.992524,0.003331251,0.0005055479,0.00174145,0.001191487,0.0007062667],"domain_scores_gemma":[0.985658,0.007676995,0.0003315791,0.003413572,0.002274496,0.0006453547],"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":[0.001833623,0.0003908093,0.00753368,0.000472082,0.0005076202,0.00005599829,0.0001825282,0.01421436,0.01152378,0.01220191,0.04174099,0.9093427],"study_design_scores_gemma":[0.0007644538,0.00132223,0.01091262,0.0004389734,0.0009266257,0.0006513362,0.001063979,0.7102238,0.06508983,0.1709856,0.03732272,0.0002978388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1047987,0.009184176,0.8302152,0.006971478,0.002527832,0.0003814014,0.003716363,0.02581151,0.01639334],"genre_scores_gemma":[0.7054167,0.003031041,0.2622359,0.001976529,0.001187335,0.0002995125,0.009303985,0.00401352,0.01253556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008871355,"threshold_uncertainty_score":0.04691678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02048011377708883,"score_gpt":0.2380527933618728,"score_spread":0.2175726795847839,"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."}}