{"id":"W4386488973","doi":"10.1162/tacl_a_00595","title":"<b>MIRACL</b>: A Multilingual Retrieval Dataset Covering 18 Diverse Languages","year":2023,"lang":"en","type":"article","venue":"Transactions of the Association for Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Annotation; Relevance (law); Natural language processing; Information retrieval; Process (computing); Quality (philosophy); Artificial intelligence; Resource (disambiguation); World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002839488,0.002343049,0.001558488,0.008977263,0.002243018,0.003139624,0.002653157,0.002979386,0.02303038],"category_scores_gemma":[0.0118616,0.0007697366,0.001334622,0.006833417,0.0009814847,0.003448456,0.003641498,0.001993055,0.04389751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001785306,"about_ca_system_score_gemma":0.00242796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03527757,"about_ca_topic_score_gemma":0.05765631,"domain_scores_codex":[0.9957322,0.001067526,0.0007437337,0.0009734106,0.001069911,0.0004130991],"domain_scores_gemma":[0.9931892,0.001568347,0.0004701878,0.001318241,0.002697692,0.0007563895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005033853,0.0002596586,0.003980467,0.002816411,0.0001622191,0.0003284547,0.0004677125,0.0009957766,0.007957336,0.001219761,0.9553984,0.02591049],"study_design_scores_gemma":[0.0008147034,0.0003360078,0.02616134,0.0005403364,0.0001891932,0.001312417,0.001513417,0.01182299,0.01213676,0.002171007,0.9426841,0.0003178157],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01904167,0.001393051,0.004018412,0.0005867735,0.0002587639,0.0004399314,0.9533213,0.01217182,0.008768313],"genre_scores_gemma":[0.009179716,0.00009346559,0.006551635,0.0001332041,0.00002856902,0.0002611907,0.9817632,0.0003384025,0.001650638],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03527757,"threshold_uncertainty_score":0.07704425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02744140968399567,"score_gpt":0.3310232120994008,"score_spread":0.3035818024154052,"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."}}