{"id":"W4385572691","doi":"10.18653/v1/2022.emnlp-main.68","title":"ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation","year":2022,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Renmin University of China","keywords":"Security token; Computer science; Autoregressive model; Inference; Layer (electronics); Language model; Dependency (UML); Speedup; Artificial intelligence; Permutation (music); Text generation; Token passing; Natural language processing; Speech recognition; Parallel computing; Computer network; Statistics; Mathematics","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":[0.001074232,0.001276681,0.0009198147,0.0007545915,0.0003594213,0.0008291107,0.001757888,0.001157276,0.003921249],"category_scores_gemma":[0.003232578,0.0006120309,0.000989873,0.0006794909,0.0004438717,0.002140546,0.0009500232,0.002276409,0.003894765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005896703,"about_ca_system_score_gemma":0.001096127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004094996,"about_ca_topic_score_gemma":0.006766962,"domain_scores_codex":[0.9993404,0.0002250349,0.00003948577,0.0002174681,0.0001124816,0.00006522096],"domain_scores_gemma":[0.998708,0.0007720361,0.00007417166,0.0001788646,0.0002165134,0.00005038551],"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":[0.0003424609,0.0002370897,0.000934928,0.0003663541,0.0001357009,0.0003464779,0.0002452956,0.3116246,0.0281962,0.006836055,0.01527809,0.6354568],"study_design_scores_gemma":[0.0000217021,0.00006307603,0.0001277578,0.00001066789,0.00001659774,0.00005287008,0.00001790752,0.9850116,0.00875375,0.003051234,0.002859228,0.00001356154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009095147,0.0005339597,0.9799612,0.0002155089,0.0001092035,0.00008828675,0.0003481557,0.008755961,0.0008925366],"genre_scores_gemma":[0.17308,0.0006748261,0.8117395,0.0005590254,0.0001480799,0.0004557559,0.003180313,0.001478644,0.008683788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004094996,"threshold_uncertainty_score":0.01311785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138970139680135,"score_gpt":0.2587601816389067,"score_spread":0.2473704802421054,"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."}}