{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002640645,0.000303404,0.0003173203,0.0001234094,0.00006924372,0.0004933032,0.00116953,0.0002945107,0.00003527051],"category_scores_gemma":[0.00001290039,0.0002563403,0.0001099754,0.0001583518,0.0000192611,0.0003340286,0.001838611,0.0007081964,0.00001438992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001014696,"about_ca_system_score_gemma":0.0004808402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002660995,"about_ca_topic_score_gemma":0.0002159072,"domain_scores_codex":[0.9976367,0.00005815234,0.0003341131,0.001078344,0.0005368982,0.0003558446],"domain_scores_gemma":[0.9976439,0.0000215285,0.0001307731,0.001892952,0.0001622079,0.0001485977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001635926,0.000177143,0.0003997284,0.0004124538,0.0002683177,0.0002510702,0.02661152,0.8277707,0.001245404,0.01668178,0.0007348956,0.1254307],"study_design_scores_gemma":[0.0002269822,0.00002513137,0.00005831204,0.0001482144,0.00002264156,0.0000175145,0.000183775,0.9958927,0.000795308,0.002313669,0.00003345813,0.0002823118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02701075,0.0002016899,0.9597802,0.001239837,0.0005150115,0.0002790524,0.000007635395,0.0007705663,0.01019532],"genre_scores_gemma":[0.5198171,0.00002726433,0.477949,0.0003389982,0.0002543538,0.0000420282,0.00004025235,0.00002312763,0.001507957],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4928063,"threshold_uncertainty_score":0.9999889,"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."}}