{"id":"W4378942418","doi":"10.48550/arxiv.2305.18486","title":"A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automatic summarization; Benchmark (surveying); Computer science; Variety (cybernetics); Artificial intelligence; Strengths and weaknesses; Machine learning; Generative grammar; Data science; Machine translation; Benchmarking; Natural language processing; Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009898422,0.0002158886,0.0004539829,0.0003215151,0.00007583703,0.00005911105,0.00102833,0.0001084807,0.000004422644],"category_scores_gemma":[0.0001066025,0.0002302283,0.00006887389,0.0003367308,0.0000384034,0.0001693653,0.001905229,0.000245509,0.00002213739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001290959,"about_ca_system_score_gemma":0.0001198193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001866187,"about_ca_topic_score_gemma":0.00004919765,"domain_scores_codex":[0.997716,0.0005712853,0.0002919708,0.0009507593,0.0003050133,0.0001649415],"domain_scores_gemma":[0.9973685,0.0002709603,0.0003616639,0.001658821,0.0002662388,0.00007378355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003273188,0.000506069,0.002825091,0.009398039,0.0007280109,0.0002764146,0.004145762,0.9325805,0.00002441755,0.04863272,0.0002188367,0.0006314366],"study_design_scores_gemma":[0.0005681796,0.00009572985,0.003215972,0.001533613,0.0002654468,0.000001457352,0.0005961954,0.9835458,0.00000853067,0.009968464,0.000001409122,0.0001991613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9055277,0.00008268949,0.09180386,0.00002812602,0.0004762145,0.001792905,0.00004775287,0.0001061157,0.0001346461],"genre_scores_gemma":[0.9995363,0.00002472128,0.0002996825,0.00001699081,0.00001844491,0.000004725724,0.00003329502,0.000009364629,0.00005652523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09400856,"threshold_uncertainty_score":0.9388436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2175232757801617,"score_gpt":0.2541113034357444,"score_spread":0.03658802765558267,"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."}}