{"id":"W2981641565","doi":"10.65109/pmny4515","title":"Capacity, Bandwidth, and Compositionality in Emergent Language Learning","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Principle of compositionality; Computer science; Generalization; Range (aeronautics); Set (abstract data type); Artificial intelligence; Natural language processing; Programming language; Epistemology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002997246,0.00009352061,0.0001499926,0.00002424281,0.0001717779,0.00006506081,0.0001077907,0.0001527065,0.0007864987],"category_scores_gemma":[0.00009977151,0.00008167912,0.00004811916,0.00008640507,0.00008821047,0.00006664078,0.0002074469,0.0004290092,0.00002222015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009733137,"about_ca_system_score_gemma":0.0000540687,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03542901,"about_ca_topic_score_gemma":0.02287617,"domain_scores_codex":[0.9989689,0.0002605457,0.0001412617,0.0002410067,0.0002371486,0.0001511655],"domain_scores_gemma":[0.9997375,0.00002623045,0.00005445714,0.00005911023,0.00003269352,0.00008999044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00005858921,0.0002163562,0.09256402,0.0004923106,0.0001318035,0.0001319317,0.7235425,0.0006788041,0.008680133,0.1493809,0.005885064,0.01823755],"study_design_scores_gemma":[0.002250277,0.0001737553,0.5293013,0.0008349893,0.0002081227,0.00001132597,0.2706341,0.004445993,0.001606759,0.07954139,0.1077334,0.003258525],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9354563,0.000666936,0.0001353088,0.01011439,0.0001923735,0.000244853,0.00001083044,0.0001148574,0.0530641],"genre_scores_gemma":[0.9976146,0.0001943793,0.0003083379,0.0002498754,0.0002419757,0.00001119858,0.00007162807,0.000004165724,0.001303869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4529084,"threshold_uncertainty_score":0.9949538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04720144876731593,"score_gpt":0.332808597371553,"score_spread":0.2856071486042371,"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."}}