{"id":"W4404781236","doi":"10.18653/v1/2024.nllp-1.5","title":"Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation","year":2024,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Question answering; Automobile insurance; Computer science; Information retrieval; Business; Actuarial science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002764945,0.001430522,0.000594949,0.003057795,0.001849825,0.001475987,0.002007358,0.001743921,0.01784345],"category_scores_gemma":[0.01250297,0.0004260223,0.0007516025,0.002349339,0.0009368508,0.001809685,0.001570041,0.001408332,0.005381121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006733097,"about_ca_system_score_gemma":0.00592705,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6207554,"about_ca_topic_score_gemma":0.6940683,"domain_scores_codex":[0.9973295,0.001061228,0.000126062,0.0007845874,0.0005136934,0.0001849113],"domain_scores_gemma":[0.9922746,0.002967494,0.0002068089,0.001116958,0.00310438,0.0003297915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001220807,0.0009835748,0.02188878,0.002393733,0.0002541875,0.001249454,0.004588398,0.03165457,0.03833608,0.005312548,0.3869645,0.5051534],"study_design_scores_gemma":[0.001031699,0.0007800061,0.1031995,0.0003973954,0.0003079085,0.001956754,0.003973032,0.4124855,0.07348549,0.00406063,0.3979394,0.0003825414],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5841792,0.004872469,0.1328062,0.004226401,0.0005848553,0.003459071,0.1345327,0.06481869,0.07052042],"genre_scores_gemma":[0.642731,0.0007416672,0.1335319,0.001070614,0.0001575815,0.001349406,0.1911517,0.001542113,0.02772397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3792446,"threshold_uncertainty_score":0.762956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01546751376813193,"score_gpt":0.2504412238906698,"score_spread":0.2349737101225378,"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."}}