{"id":"W4311706555","doi":"10.1002/aaai.12068","title":"Search and learning for unsupervised text generation","year":2022,"lang":"en","type":"article","venue":"AI Magazine","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"DeepMind; Alberta Machine Intelligence Institute; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Artificial intelligence; Heuristic; Task (project management); Sentence; Annotation; Machine learning; Function (biology); Component (thermodynamics); Unsupervised learning; Natural language processing; Resource (disambiguation)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003530712,0.00004441834,0.00005452781,0.00004753799,0.0003037529,0.00007005188,0.0001838082,0.0000107975,0.00003849087],"category_scores_gemma":[0.00002149719,0.00004711734,0.00001536798,0.0001078879,0.00000676871,0.0001435975,0.0002599296,0.0001377218,0.000009415397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002075097,"about_ca_system_score_gemma":0.00002750161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001051628,"about_ca_topic_score_gemma":0.000003343641,"domain_scores_codex":[0.9993802,0.0000646051,0.00008089818,0.0002112196,0.0001353854,0.0001276328],"domain_scores_gemma":[0.999734,0.00003682087,0.00001077916,0.0001548906,0.00003350362,0.00003003312],"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":[0.00001382713,0.00005712803,0.002277808,0.0000419388,0.00001827378,0.00001075558,0.003662087,0.1869798,0.0802199,0.06180147,0.0065245,0.6583925],"study_design_scores_gemma":[0.0002580795,0.00008132229,0.000373508,8.185821e-7,0.000001354551,0.000007590959,0.0000174925,0.9661598,0.0003537307,0.0002837734,0.03240486,0.00005764314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1772861,0.0001216288,0.8179865,0.004009094,0.0001271299,0.000131648,7.171826e-7,0.00006159201,0.0002755675],"genre_scores_gemma":[0.9549074,0.000004776547,0.04176261,0.0009151357,0.0001076838,0.00004359166,0.000008666314,0.000006280146,0.002243814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.77918,"threshold_uncertainty_score":0.2336252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03918375269357233,"score_gpt":0.2721839684174112,"score_spread":0.2330002157238389,"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."}}