{"id":"W4307895180","doi":"10.48550/arxiv.2205.09393","title":"Two-Step Question Retrieval for Open-Domain QA","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Search engine indexing; Inference; Computer science; Labrador Retriever; Information retrieval; Pipeline (software); Domain (mathematical analysis); Squid; Artificial intelligence; Mathematics; Programming language; Medicine; Biology; Fishery","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003319472,0.0008214837,0.001054816,0.001236835,0.0006282299,0.001137713,0.002536657,0.001823969,0.008078161],"category_scores_gemma":[0.01111413,0.0006167188,0.001214441,0.0009903305,0.0009429277,0.004812097,0.002749695,0.002840715,0.004404703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001155247,"about_ca_system_score_gemma":0.001864384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007862336,"about_ca_topic_score_gemma":0.00912227,"domain_scores_codex":[0.9986356,0.0005653143,0.00007940084,0.0003912938,0.0002171256,0.0001112752],"domain_scores_gemma":[0.9948443,0.00283721,0.0001714106,0.001307878,0.0006351819,0.0002040657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009939384,0.000825498,0.005413118,0.0009056827,0.000190955,0.0002258507,0.0008241445,0.1113341,0.0229132,0.03412228,0.03320208,0.7890491],"study_design_scores_gemma":[0.00007152396,0.0001502873,0.0006496636,0.00001816268,0.00003037647,0.0001639698,0.00005755772,0.9634714,0.007094926,0.02359164,0.004676064,0.00002447283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02206236,0.0009434015,0.9639532,0.0007040678,0.00008172502,0.0002927185,0.0006084961,0.009037466,0.00231657],"genre_scores_gemma":[0.5021284,0.0005451936,0.4861602,0.0006148547,0.0001889486,0.0004120234,0.003339048,0.0004027032,0.006208631],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008078161,"threshold_uncertainty_score":0.02702415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09835796420991373,"score_gpt":0.2338652679081067,"score_spread":0.135507303698193,"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."}}