{"id":"W3092734951","doi":"10.18653/v1/2020.findings-emnlp.57","title":"From Language to Language-ish: How Brain-Like is an LSTM’s Representation of Nonsensical Language Stimuli?","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Natural language processing; Artificial intelligence; Language model; Representation (politics); Language identification; Natural language","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":[0.0006525372,0.0003306016,0.0002790029,0.0003430929,0.0001316368,0.001385476,0.0003923326,0.0006342403,0.0019202],"category_scores_gemma":[0.006214757,0.0002205658,0.0004094552,0.000525873,0.0009063398,0.003649066,0.0004249191,0.0009931258,0.0004131481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000310985,"about_ca_system_score_gemma":0.0002080804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001176392,"about_ca_topic_score_gemma":0.0008474886,"domain_scores_codex":[0.999792,0.00007568187,0.000007562354,0.00007889478,0.00002353278,0.00002239705],"domain_scores_gemma":[0.99932,0.0003461594,0.0001173556,0.0000901972,0.00007288805,0.00005336501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008721538,0.0002026223,0.04169913,0.0008562218,0.0006994018,0.0007361585,0.005082502,0.05149475,0.4930822,0.04804823,0.00528014,0.3519465],"study_design_scores_gemma":[0.00006386058,0.000512524,0.1149271,0.0001722001,0.0002637629,0.0007578762,0.002397008,0.5749035,0.09109434,0.2092661,0.005461238,0.0001805347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8249385,0.00102632,0.1628107,0.00276983,0.0002166544,0.00003962131,0.0004713509,0.0006055658,0.007121406],"genre_scores_gemma":[0.9916275,0.0002374878,0.006887647,0.0002007931,0.00003376275,0.00001258644,0.0001659707,0.00006427258,0.0007699092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0019202,"threshold_uncertainty_score":0.006423712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05953267168455737,"score_gpt":0.3398102721129692,"score_spread":0.2802776004284118,"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."}}