{"id":"W4389518978","doi":"10.18653/v1/2023.findings-emnlp.393","title":"Aligning Language Models to User Opinions","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Persona; Ideology; Demographics; Public opinion; Set (abstract data type); Computer science; User group; Work (physics); Data science; Internet privacy; Human–computer interaction; World Wide Web; Political science; Sociology; Engineering; Politics","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":[0.005128955,0.0009985408,0.0007556748,0.001946856,0.0004824304,0.002885653,0.001209942,0.001243711,0.002893964],"category_scores_gemma":[0.02829975,0.0005619295,0.001069498,0.001285937,0.0004653583,0.003634327,0.001933318,0.002095505,0.00287006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000981356,"about_ca_system_score_gemma":0.0009682213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004071731,"about_ca_topic_score_gemma":0.005522279,"domain_scores_codex":[0.9948102,0.003197661,0.0002711963,0.0009160635,0.0005963245,0.0002085813],"domain_scores_gemma":[0.9896576,0.006408096,0.000645422,0.001237851,0.001769405,0.0002816179],"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":[0.001128269,0.0006844808,0.03999858,0.0006145209,0.000621406,0.0004049412,0.003747165,0.2470621,0.0290805,0.01746509,0.01732501,0.6418679],"study_design_scores_gemma":[0.00001847801,0.000108454,0.002143463,0.00003916932,0.00005846763,0.00005420404,0.000493279,0.969454,0.004563504,0.01692837,0.00610061,0.00003800566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1637754,0.000807505,0.8194389,0.001897101,0.0002507626,0.0002556626,0.00142916,0.00699972,0.005145729],"genre_scores_gemma":[0.79406,0.00031605,0.1973374,0.0005737503,0.0001805355,0.0003078749,0.003335237,0.000728883,0.003160171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005128955,"threshold_uncertainty_score":0.02712482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06393325834733068,"score_gpt":0.3031732236569212,"score_spread":0.2392399653095905,"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."}}