{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004841861,0.0004292782,0.0007279772,0.0008377974,0.0008246251,0.002070524,0.0007321321,0.001252019,0.002477294],"category_scores_gemma":[0.04840815,0.0004534901,0.0005217153,0.0004576799,0.005300085,0.006197135,0.003050021,0.00199629,0.00019818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009680825,"about_ca_system_score_gemma":0.000689769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009699732,"about_ca_topic_score_gemma":0.0008648188,"domain_scores_codex":[0.9982182,0.001068413,0.00008229431,0.0002942899,0.0001827533,0.0001539632],"domain_scores_gemma":[0.9551386,0.03828196,0.002072066,0.002470404,0.0009706531,0.001066331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003055148,0.0001378369,0.01358415,0.0004007721,0.0001678082,0.0002321447,0.002307148,0.2569887,0.008471703,0.6743413,0.0006886473,0.04237429],"study_design_scores_gemma":[0.00002135896,0.00006111323,0.001637728,0.00003939627,0.00002458873,0.00008315776,0.000231046,0.2530341,0.001594584,0.7426287,0.0006136238,0.0000307777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6305549,0.001388789,0.3480595,0.004074565,0.0000483512,0.00006012872,0.0001282016,0.0002794183,0.01540614],"genre_scores_gemma":[0.9883865,0.0002232262,0.01077524,0.00008576805,0.00002825025,0.00004210761,0.00003033094,0.00004015545,0.0003883189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004841861,"threshold_uncertainty_score":0.02560651,"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."}}