{"id":"W4404782405","doi":"10.18653/v1/2024.emnlp-main.1107","title":"Jump Starting Bandits with LLM-Generated Prior Knowledge","year":2024,"lang":"en","type":"article","venue":"","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute","funders":"","keywords":"Computer science; Jump; Artificial intelligence; Physics","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":[0.0003206429,0.0001323069,0.000118772,0.0001107356,0.0001550979,0.0005275259,0.0002887525,0.00003570076,0.0000578841],"category_scores_gemma":[0.00001514488,0.00008853971,0.00003932719,0.0004784524,0.00001326463,0.0003754704,0.00009554996,0.0001646661,0.0006433096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000527328,"about_ca_system_score_gemma":0.000118723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004342989,"about_ca_topic_score_gemma":0.00002085132,"domain_scores_codex":[0.9989419,0.00005205243,0.0001785705,0.0003830976,0.0001659075,0.0002784818],"domain_scores_gemma":[0.9995025,0.00007407957,0.00002869341,0.0002217301,0.0001028944,0.00007006915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005108368,0.0000605671,0.0005100221,0.0001329676,0.00008897953,0.0001637498,0.004697218,0.000897662,0.006116171,0.9172624,0.00562665,0.06443848],"study_design_scores_gemma":[0.0001773815,0.000328195,0.0003668103,0.0006034409,0.00001067365,0.00004212392,0.0001733908,0.3062488,0.03328581,0.0002678323,0.6580199,0.0004756118],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.033154,0.001005052,0.933301,0.0002844708,0.00115224,0.0001390878,6.026888e-7,0.0009576145,0.03000596],"genre_scores_gemma":[0.8833498,0.000003119154,0.01026695,0.00005668355,0.0003250028,0.000009864219,0.000001052783,0.00001679697,0.1059707],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.923034,"threshold_uncertainty_score":0.8268658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02047122741442307,"score_gpt":0.2573617031314274,"score_spread":0.2368904757170043,"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."}}