{"id":"W4362700380","doi":"10.1007/s11227-023-05209-z","title":"Exploring implicit persona knowledge for personalized dialogue generation","year":2023,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"National Social Science Fund of China","keywords":"Persona; Computer science; Consistency (knowledge bases); Key (lock); Human–computer interaction; Style (visual arts); World Wide Web; Data science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002413919,0.000113801,0.000199701,0.0001705921,0.0003726216,0.000107345,0.0007890119,0.00002954953,0.000001983605],"category_scores_gemma":[0.0001402031,0.00008085266,0.0001502461,0.000371944,0.00002353187,0.000660655,0.0001898421,0.0001865583,0.00001423135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006664367,"about_ca_system_score_gemma":0.00008410596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001475148,"about_ca_topic_score_gemma":0.000003830174,"domain_scores_codex":[0.9987867,0.0001303875,0.000405085,0.0001461995,0.0002294843,0.0003021808],"domain_scores_gemma":[0.998857,0.0005012044,0.0001321169,0.000226227,0.000215766,0.0000676924],"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":[0.00005268254,0.0001008876,0.0006280183,0.000148741,0.0002239472,0.00003244283,0.1854018,0.06671264,0.1963435,0.07087298,0.004838601,0.4746437],"study_design_scores_gemma":[0.0005747119,0.00008562929,0.0003992297,0.00006569889,0.00002219727,0.0001360714,0.001463222,0.9921863,0.002104053,0.000927588,0.001895891,0.0001393963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4983756,0.0002208619,0.499537,0.0009115196,0.0007831287,0.00007945176,5.441222e-7,0.00005077881,0.00004107398],"genre_scores_gemma":[0.9740406,0.00008257124,0.02382258,0.000102079,0.001859858,0.000004700784,0.000001353198,0.00001485274,0.00007139742],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9254737,"threshold_uncertainty_score":0.3297076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2566185109295542,"score_gpt":0.3163193920778916,"score_spread":0.05970088114833738,"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."}}