{"id":"W4378465150","doi":"10.48550/arxiv.2305.14929","title":"Aligning Language Models to User Opinions","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Ideology; Demographics; Persona; Set (abstract data type); Public opinion; User group; Computer science; Data science; Social psychology; Public relations; Political science; Psychology; World Wide Web; Human–computer interaction; Sociology; Demography; Politics; Law","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00411496,0.0009962531,0.0007002588,0.002122227,0.0004032645,0.002397124,0.001075906,0.001164938,0.002647682],"category_scores_gemma":[0.02258937,0.000535126,0.001047728,0.001366961,0.0004179055,0.003181051,0.001445321,0.001658588,0.002711445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009361811,"about_ca_system_score_gemma":0.0008032584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005354144,"about_ca_topic_score_gemma":0.006605179,"domain_scores_codex":[0.9965941,0.002040891,0.0001753847,0.0006162078,0.0004101076,0.0001632634],"domain_scores_gemma":[0.9901155,0.0067026,0.0006208331,0.000951213,0.001387511,0.0002223646],"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":[0.00129075,0.0007155365,0.04767964,0.0005833086,0.000580277,0.0003991663,0.002756657,0.354358,0.02161626,0.01443614,0.01755638,0.5380279],"study_design_scores_gemma":[0.00001410298,0.0000725721,0.00180648,0.00002454325,0.00003872819,0.0000369822,0.0002250125,0.9823492,0.002716541,0.009789716,0.002901213,0.00002490874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2241666,0.0008659,0.7570193,0.001964003,0.000203086,0.0002562363,0.002125686,0.007449665,0.005949482],"genre_scores_gemma":[0.8681766,0.0002979003,0.1249098,0.0003727919,0.0001513018,0.0002588778,0.002749624,0.000417449,0.00266562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005354144,"threshold_uncertainty_score":0.02176225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1715150484945847,"score_gpt":0.2220219272290909,"score_spread":0.05050687873450621,"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."}}