{"id":"W3201759467","doi":"","title":"TopiOCQA: Open-domain Conversational Question Answeringwith Topic Switching.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Conversation; Computer science; Question answering; Domain (mathematical analysis); Natural language processing; Open domain; Interdependence; Artificial intelligence; Information retrieval; Code (set theory); Relevance (law); Limiting; Linguistics; Set (abstract data type); Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003555987,0.0002660564,0.0003147125,0.0001468767,0.0001891403,0.0004950343,0.002467406,0.0002434983,0.00005788828],"category_scores_gemma":[0.00003311435,0.0003258226,0.0001297334,0.0003032508,0.0000349099,0.0007749015,0.004208861,0.0005482067,0.00002381952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000326795,"about_ca_system_score_gemma":0.0004806791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007026615,"about_ca_topic_score_gemma":0.0001490372,"domain_scores_codex":[0.9979029,0.0001847377,0.0002255983,0.001264134,0.0001326972,0.0002898863],"domain_scores_gemma":[0.9981068,0.0000628476,0.000205113,0.001308571,0.0001758313,0.0001407831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001384613,0.0000936887,0.00639859,0.00009116922,0.00009947833,0.0005432631,0.00096042,0.1346252,0.0001070582,0.854798,0.00009231732,0.002176955],"study_design_scores_gemma":[0.0009215737,0.00004140438,0.004447164,0.0002628472,0.0000411232,0.00001860892,0.0002733394,0.8679885,0.0001729068,0.123645,0.001453555,0.0007338978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3178515,0.00004011835,0.6785036,0.0003678593,0.000925168,0.0002105053,0.000002198271,0.000122197,0.001976866],"genre_scores_gemma":[0.9670056,0.00004791885,0.03104153,0.0002452581,0.0001742246,0.000001758451,0.0000227272,0.00001240241,0.001448624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7333634,"threshold_uncertainty_score":0.9999194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06853225001111356,"score_gpt":0.2029563697949861,"score_spread":0.1344241197838726,"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."}}