{"id":"W4389523719","doi":"10.18653/v1/2023.findings-emnlp.772","title":"Asking Clarification Questions to Handle Ambiguity in Open-Domain QA","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Seoul National University","keywords":"Ambiguity; Computer science; Ask price; Pipeline (software); Question answering; Process (computing); Domain (mathematical analysis); Interpretation (philosophy); Open domain; Information retrieval; 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":[0.01148068,0.002647492,0.001679671,0.00367942,0.001943651,0.003211899,0.003160438,0.004633678,0.009068153],"category_scores_gemma":[0.06052941,0.000899746,0.001663609,0.0020657,0.00160015,0.008064264,0.006540736,0.005300361,0.006376452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001289972,"about_ca_system_score_gemma":0.001872968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003321468,"about_ca_topic_score_gemma":0.004336663,"domain_scores_codex":[0.9841955,0.009796237,0.001093128,0.002797055,0.001584052,0.0005341241],"domain_scores_gemma":[0.9419946,0.04309865,0.001898593,0.007899624,0.003921038,0.001187593],"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":[0.002164265,0.001480698,0.0238259,0.007643066,0.0002844375,0.00172698,0.01984849,0.02849014,0.06052168,0.02620275,0.1289601,0.6988515],"study_design_scores_gemma":[0.0007282524,0.0009903774,0.02179827,0.001452249,0.0002949404,0.00416196,0.007925543,0.4326309,0.08316026,0.09216405,0.3541918,0.0005014231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1810919,0.005596637,0.7339032,0.003860271,0.0007796484,0.002359525,0.01458751,0.04205531,0.01576609],"genre_scores_gemma":[0.3859262,0.0006709803,0.5694616,0.001427162,0.000269791,0.001288211,0.0348678,0.001514957,0.004573287],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01148068,"threshold_uncertainty_score":0.06071633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07022568823081114,"score_gpt":0.3358474356945819,"score_spread":0.2656217474637707,"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."}}