{"id":"W4411531987","doi":"10.1007/978-3-031-96235-6_22","title":"Enhancing Answer Reliability Through Inter-Model Consensus of Large Language Models","year":2025,"lang":"en","type":"book-chapter","venue":"IFIP advances in information and communication technology","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Reliability (semiconductor); Computer science; Natural language processing; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009023617,0.001189292,0.002080371,0.001370615,0.001108074,0.002777049,0.00341814,0.002082516,0.004798862],"category_scores_gemma":[0.05088921,0.001230084,0.001753445,0.001330663,0.001392037,0.007645885,0.005484055,0.004521917,0.002376008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00134083,"about_ca_system_score_gemma":0.002278213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002615342,"about_ca_topic_score_gemma":0.004025244,"domain_scores_codex":[0.9907615,0.004608663,0.0004759776,0.001366691,0.002285809,0.0005013013],"domain_scores_gemma":[0.9504877,0.03445693,0.001547874,0.008176319,0.004765736,0.0005654822],"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":[0.001099639,0.0005202029,0.002140804,0.0006131365,0.000364464,0.0003189203,0.001671386,0.4022267,0.0327202,0.1229345,0.01619451,0.4191955],"study_design_scores_gemma":[0.00003350288,0.000055056,0.00009160047,0.00001396702,0.0000505495,0.00003177018,0.00007864642,0.9255562,0.005626275,0.06679585,0.001649549,0.00001689535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01102793,0.0001515097,0.9848475,0.0004007727,0.00005249965,0.00005881033,0.00008449479,0.002153078,0.001223426],"genre_scores_gemma":[0.4803975,0.0002991853,0.5116863,0.0004364529,0.0001762853,0.0002971661,0.0008510266,0.001455947,0.004400139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009023617,"threshold_uncertainty_score":0.04772204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01114010563274158,"score_gpt":0.2817262020971429,"score_spread":0.2705860964644013,"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."}}