{"id":"W4396843976","doi":"10.1145/3589335.3641299","title":"Information Retrieval Meets Large Language Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Information retrieval; Natural language processing; Artificial intelligence; Data science","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":[0.0174037,0.001793631,0.003808814,0.003295397,0.002567953,0.0114219,0.003350204,0.006551488,0.02471183],"category_scores_gemma":[0.09952142,0.002635621,0.002951064,0.003750107,0.002797547,0.02771397,0.008944652,0.008599915,0.01698959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003944241,"about_ca_system_score_gemma":0.004268698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005291113,"about_ca_topic_score_gemma":0.005635441,"domain_scores_codex":[0.975033,0.01490631,0.001412685,0.003353481,0.004453026,0.0008414456],"domain_scores_gemma":[0.8749546,0.1013081,0.002402632,0.01439189,0.005628738,0.001314043],"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":[0.000244228,0.0001647794,0.001046992,0.00100464,0.0002960394,0.0006855751,0.0007083195,0.02375446,0.001643488,0.7633619,0.0917307,0.1153589],"study_design_scores_gemma":[0.00007168904,0.00002929339,0.0002001758,0.00008331593,0.00004735085,0.0003612386,0.0001324555,0.1653606,0.0008483778,0.7936571,0.03916119,0.00004714929],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00499544,0.008621421,0.9108686,0.03527756,0.0009948433,0.0002578632,0.002693726,0.003667215,0.03262324],"genre_scores_gemma":[0.3161904,0.01702124,0.580743,0.01085542,0.01096328,0.002446928,0.01254965,0.003433283,0.04579683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02471183,"threshold_uncertainty_score":0.09204072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01474873763722081,"score_gpt":0.2531784605361799,"score_spread":0.2384297228989591,"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."}}