{"id":"W4401212045","doi":"10.1145/3674118","title":"Using Generative AI in Finance, and the Lack of Emergent Behavior in LLMs","year":2024,"lang":"en","type":"article","venue":"Communications of the ACM","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Generative grammar; Publication; Join (topology); Computer science; Generative model; World Wide Web; Artificial intelligence; Political science; Law","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":[0.005547023,0.000424773,0.0006807402,0.001196005,0.001187946,0.00423254,0.001425998,0.002124964,0.004157039],"category_scores_gemma":[0.02975658,0.0006195968,0.0009684103,0.001103866,0.007763177,0.008445212,0.002458532,0.003556027,0.0006468586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002671491,"about_ca_system_score_gemma":0.001275446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004473448,"about_ca_topic_score_gemma":0.004757992,"domain_scores_codex":[0.9977955,0.001357179,0.00009466371,0.0003011572,0.0003349766,0.0001165518],"domain_scores_gemma":[0.9722937,0.0235297,0.0007643226,0.002375386,0.0005689705,0.0004679425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002259587,0.00002114962,0.001858711,0.0001077474,0.00004342416,0.0001102078,0.0005823941,0.02177279,0.0003580553,0.9475787,0.005128439,0.02241576],"study_design_scores_gemma":[0.000006464915,0.000006451304,0.000251999,0.00002906959,0.000005181342,0.00004127549,0.00006556352,0.04795352,0.0001501462,0.9465155,0.004961526,0.00001334525],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04946165,0.006184747,0.8247066,0.07265368,0.000767144,0.00004342583,0.000355665,0.001158656,0.04466855],"genre_scores_gemma":[0.8825477,0.00336754,0.1008115,0.003957434,0.0008711075,0.000151719,0.0002815659,0.0003908205,0.007620601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005547023,"threshold_uncertainty_score":0.0293358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4889967636852753,"score_gpt":0.5410628367157653,"score_spread":0.05206607303048999,"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."}}