{"id":"W6920901177","doi":"10.60692/rz8hd-c0104","title":"Pragmatic Inference with a CLIP Listener for Contrastive Captioning","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Closed captioning; Discriminative model; Inference; Leverage (statistics); Fluency; Prosody; Hyperparameter","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":[0.003563621,0.001836863,0.000937516,0.001064131,0.0009560479,0.00259049,0.00270819,0.002093315,0.01099584],"category_scores_gemma":[0.01825551,0.0008564656,0.001344991,0.0005509058,0.001753573,0.003798129,0.00296044,0.003666472,0.003211755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001474361,"about_ca_system_score_gemma":0.001260921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00255703,"about_ca_topic_score_gemma":0.003505582,"domain_scores_codex":[0.9967086,0.001403037,0.0001198759,0.0009272486,0.0006637373,0.0001774432],"domain_scores_gemma":[0.9950489,0.002891022,0.0002949438,0.0008508753,0.0006967948,0.0002174402],"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":[0.0009753993,0.0003692186,0.002336121,0.0008431317,0.0003264838,0.0007231102,0.002348178,0.1228897,0.07269526,0.1090166,0.03941502,0.6480618],"study_design_scores_gemma":[0.00005485364,0.0001091171,0.0004917806,0.00003995318,0.00006097457,0.0002004807,0.0001450303,0.9189247,0.02080215,0.04785566,0.01124792,0.00006744843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006393888,0.0001783824,0.9836436,0.0003121371,0.000112272,0.0001468526,0.0001999215,0.004381841,0.004631185],"genre_scores_gemma":[0.3298735,0.0001805633,0.6596799,0.0009103273,0.0003152097,0.0004094696,0.001229973,0.001291494,0.006109384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01099584,"threshold_uncertainty_score":0.03678471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03101776588450244,"score_gpt":0.2535954053786056,"score_spread":0.2225776394941031,"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."}}