{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006920805,0.0006503318,0.0007036097,0.0007224453,0.0004962621,0.0008304181,0.003721413,0.0002680172,0.0000336729],"category_scores_gemma":[0.00007964214,0.0005777072,0.0003142538,0.0003781225,0.0003336869,0.0006899178,0.0008680433,0.0008862795,0.000001988451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004802722,"about_ca_system_score_gemma":0.0003468768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001048015,"about_ca_topic_score_gemma":0.0000822127,"domain_scores_codex":[0.9953407,0.00003616132,0.0004846333,0.001987998,0.001259809,0.0008907191],"domain_scores_gemma":[0.9976813,0.0005289731,0.0003272165,0.001117048,0.00015414,0.0001913811],"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":[0.00001707498,0.00003432963,0.00003328636,0.0001218501,0.00002248765,0.00003128696,0.001557515,0.01765514,0.0001457624,0.02512205,0.00001993257,0.9552393],"study_design_scores_gemma":[0.0007703704,0.0002793508,0.000111666,0.0002593814,0.00001794159,0.00004391234,8.653124e-7,0.885977,0.0006647345,0.1016165,0.009299382,0.000958924],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001982187,0.0003412502,0.9903257,0.004595411,0.00253011,0.001101978,0.0000206013,0.0002220919,0.0006645857],"genre_scores_gemma":[0.5791557,0.00006519655,0.4149804,0.002766664,0.0009608685,0.0001779791,0.00001914739,0.0001018133,0.001772166],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9542804,"threshold_uncertainty_score":0.9996675,"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."}}