{"id":"W4379534564","doi":"10.21428/594757db.132dae7d","title":"RISC: Generating Realistic Synthetic Bilingual Insurance Contract","year":2023,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Automobile insurance; Natural language processing; Python (programming language); Artificial intelligence; Insurance policy; Class (philosophy); Programming language; Finance; Actuarial science; Business","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0009048618,0.001139026,0.0004366539,0.001641079,0.000684454,0.0006439632,0.001558514,0.001036634,0.01178504],"category_scores_gemma":[0.00423264,0.000373396,0.0007583603,0.001821489,0.0004495198,0.0007315397,0.0009476289,0.0009175158,0.005850429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001573287,"about_ca_system_score_gemma":0.002110248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03704761,"about_ca_topic_score_gemma":0.06534702,"domain_scores_codex":[0.9992082,0.0002185964,0.00005513101,0.00017227,0.0002704853,0.00007537412],"domain_scores_gemma":[0.9982656,0.0006849181,0.00007526705,0.0003456227,0.0005203758,0.0001082745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007719256,0.0005490934,0.01099268,0.001645318,0.0001787584,0.00163085,0.0006380727,0.07734852,0.0127986,0.00975709,0.7443834,0.1393057],"study_design_scores_gemma":[0.0006336363,0.0002333751,0.01190127,0.00020466,0.00006374489,0.0009603008,0.001071031,0.3979435,0.03268265,0.01531591,0.5388334,0.0001565361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.109998,0.000796025,0.1010252,0.001213933,0.0006022584,0.001252938,0.6995451,0.05792201,0.02764449],"genre_scores_gemma":[0.1036258,0.0001979256,0.1202724,0.0003182128,0.00004174262,0.001182129,0.7648602,0.002648769,0.006852906],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.03704761,"threshold_uncertainty_score":0.07366395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01882596123007467,"score_gpt":0.2935253874547359,"score_spread":0.2746994262246612,"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."}}