{"id":"W6910236968","doi":"10.48448/mh3w-0970","title":"Pragmatic Inference with a CLIP Listener for Contrastive Captioning","year":2022,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00148097,0.0004945462,0.0005587132,0.001138645,0.000668062,0.00029957,0.001154447,0.0001336129,0.004660512],"category_scores_gemma":[0.00107006,0.0004005977,0.00006870154,0.001809573,0.002067155,0.0003256095,0.000290763,0.000477238,0.0004699451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007450492,"about_ca_system_score_gemma":0.002388191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004897128,"about_ca_topic_score_gemma":0.002355237,"domain_scores_codex":[0.9960723,0.00009609066,0.0003922631,0.001134589,0.00140908,0.0008956824],"domain_scores_gemma":[0.9974818,0.0004079904,0.0007808409,0.0007105045,0.0003769661,0.0002419104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008609813,0.002104932,0.006131657,0.001646229,0.001154318,0.0002269257,0.00633314,0.01775445,0.00982821,0.2639897,0.6641534,0.02581602],"study_design_scores_gemma":[0.005812115,0.002103152,0.000443505,0.001565876,0.0006328149,0.0002094074,0.004142988,0.115047,0.0001136832,0.003189852,0.8635126,0.003227001],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0004014795,0.0004145761,0.07799911,0.0002985796,0.0008836853,0.005006723,0.00224052,0.001544583,0.9112107],"genre_scores_gemma":[0.1562355,0.00002375283,0.1578277,0.0009300191,0.001055429,0.002428018,0.001356789,0.003062273,0.6770805],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2607999,"threshold_uncertainty_score":0.9998446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0218000878311415,"score_gpt":0.3137877719036097,"score_spread":0.2919876840724682,"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."}}