{"id":"W4392669913","doi":"10.18653/v1/2023.findings-ijcnlp.6","title":"Learning to Diversify Neural Text Generation via Degenerative Model","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology","keywords":"Computer science; Language model; Limiting; Artificial intelligence; Diversity (politics); Repetition (rhetorical device); Machine learning; Natural language processing; Engineering; Linguistics","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":[0.002878207,0.001416296,0.001126801,0.001144021,0.0004762287,0.001031955,0.002174912,0.001864826,0.001235837],"category_scores_gemma":[0.01273216,0.0007509817,0.0006797393,0.0006375022,0.0009821469,0.00284458,0.00240739,0.002474305,0.0005837056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009422869,"about_ca_system_score_gemma":0.0008168093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001383754,"about_ca_topic_score_gemma":0.002717816,"domain_scores_codex":[0.9990904,0.0003264956,0.00005854871,0.0002938515,0.0001377664,0.00009277876],"domain_scores_gemma":[0.993497,0.004263199,0.0005193501,0.0007395166,0.0007239333,0.0002569396],"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":[0.0003648414,0.0004390525,0.00544267,0.0001775746,0.0001302357,0.0002504369,0.0005037328,0.6994869,0.0146791,0.009277545,0.004191139,0.2650568],"study_design_scores_gemma":[0.00001702891,0.0000524685,0.0001115697,0.000005737715,0.00001105178,0.00002521734,0.00001178538,0.994567,0.001426243,0.003549702,0.0002162575,0.000005857882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1896916,0.0007511491,0.802829,0.0008616054,0.00009497575,0.000173158,0.0001285517,0.002807871,0.002662053],"genre_scores_gemma":[0.8841441,0.0002237531,0.1112484,0.0005393442,0.00010247,0.0003705652,0.0004167895,0.0001624246,0.002792192],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002878207,"threshold_uncertainty_score":0.0152216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06359297354936129,"score_gpt":0.2703843790303541,"score_spread":0.2067914054809928,"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."}}