{"id":"W3086996502","doi":"10.18653/v1/2021.findings-acl.378","title":"MLMLM: Link Prediction with Mean Likelihood Masked Language Model","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute; Canadian Institute for Advanced Research","funders":"Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Interpretability; Computer science; Scalability; Language model; Artificial intelligence; Link (geometry); Verifiable secret sharing; Embedding; Machine learning; Scale (ratio); Data mining; Natural language processing; Database","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":[0.003569288,0.001976181,0.001418307,0.002905446,0.0009044933,0.002724656,0.003872024,0.002439555,0.006376645],"category_scores_gemma":[0.01661916,0.0009277671,0.002263465,0.002701446,0.0007901769,0.005102108,0.003196124,0.003918998,0.006125701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001275205,"about_ca_system_score_gemma":0.002216531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009492472,"about_ca_topic_score_gemma":0.0139056,"domain_scores_codex":[0.9971559,0.00112789,0.0001798971,0.0008065862,0.0005681473,0.0001615549],"domain_scores_gemma":[0.9927355,0.004960173,0.0003329548,0.001293788,0.0005040584,0.0001736224],"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":[0.001185072,0.0006092838,0.009058519,0.001257285,0.0007620367,0.0007863392,0.0005968007,0.3189412,0.007049257,0.03087605,0.1264765,0.5024016],"study_design_scores_gemma":[0.00007836764,0.00006886455,0.0004386183,0.00004217747,0.00004789303,0.0001039689,0.00004815527,0.9489399,0.00199868,0.04199115,0.006208778,0.00003335695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02500482,0.002598362,0.9168354,0.001834652,0.0003566845,0.0003513801,0.01321153,0.03653524,0.00327191],"genre_scores_gemma":[0.3098044,0.001073419,0.6287777,0.00125403,0.0005285661,0.0008258228,0.04905464,0.001902774,0.006778634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009492472,"threshold_uncertainty_score":0.02133203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01731573470158831,"score_gpt":0.2318509993721697,"score_spread":0.2145352646705814,"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."}}