{"id":"W4385571455","doi":"10.18653/v1/2023.findings-acl.150","title":"Attribute Controlled Dialogue Prompting","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); Vector Institute","funders":"Vector Institute; University of Waterloo; Government of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Conversation; Task (project management); Domain (mathematical analysis); Open domain; Code (set theory); Artificial intelligence; Control (management); Natural language processing; Language model; Human–computer interaction; Machine learning; Programming language; Question answering; Linguistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003336655,0.001759248,0.001176395,0.000685219,0.0005118557,0.001253393,0.002008616,0.001237104,0.0110782],"category_scores_gemma":[0.01929648,0.0004144241,0.0007798857,0.0005640793,0.0007018533,0.002075169,0.002166034,0.002248264,0.004895549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005247076,"about_ca_system_score_gemma":0.0008967551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007391939,"about_ca_topic_score_gemma":0.0009741125,"domain_scores_codex":[0.9967028,0.00136599,0.0001667371,0.001257528,0.0003484643,0.0001584172],"domain_scores_gemma":[0.9930297,0.004172736,0.0003006707,0.001245182,0.0009402707,0.0003115082],"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":[0.00206301,0.0004734688,0.004043642,0.001079444,0.0001148258,0.0004365561,0.002526082,0.04087149,0.0696586,0.009474909,0.0417328,0.8275252],"study_design_scores_gemma":[0.0005277761,0.0007164952,0.002433831,0.0001478227,0.0001322234,0.0006185641,0.0007270631,0.8056569,0.07859669,0.03411135,0.07615752,0.0001738402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0316851,0.0005576496,0.9021322,0.0002533467,0.0004731515,0.0005344413,0.001349703,0.05930533,0.003709105],"genre_scores_gemma":[0.5106521,0.0003146124,0.4686756,0.0006297078,0.0003274667,0.001230263,0.004895849,0.004125198,0.009149272],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0110782,"threshold_uncertainty_score":0.03706032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0452125048558184,"score_gpt":0.2669467549645016,"score_spread":0.2217342501086832,"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."}}