{"id":"W4385569686","doi":"10.18653/v1/2023.acl-long.274","title":"ConvGQR: Generative Query Reformulation for Conversational Search","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"China Scholarship Council; Tsinghua University","keywords":"Computer science; Query expansion; Conversation; Query language; Rewriting; Web search query; Information retrieval; Web query classification; Query optimization; RDF query language; Sargable; Generative grammar; Task (project management); Artificial intelligence; Search engine; Programming language; Linguistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002786359,0.00004237482,0.00004899867,0.00007311485,0.00008466548,0.00005140997,0.0001782436,0.00002751398,0.00002245027],"category_scores_gemma":[0.00002144145,0.00003184851,0.00002672167,0.0001692572,0.000009606991,0.000285589,0.00007700187,0.00003128371,0.0001027292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003894028,"about_ca_system_score_gemma":0.00007004524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003040149,"about_ca_topic_score_gemma":0.000007845488,"domain_scores_codex":[0.9993988,0.00001717681,0.0001028523,0.0001865043,0.0001593719,0.0001352921],"domain_scores_gemma":[0.9995809,0.0001014962,0.00001571606,0.0001673985,0.0001036802,0.00003076567],"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":[0.000002896001,0.000005350707,0.0004020284,0.000008029023,0.000009150946,9.237382e-7,0.0008456454,0.01387495,0.001186366,0.9630884,0.002962432,0.01761384],"study_design_scores_gemma":[0.000184367,0.00001429244,0.0006949014,0.000001582484,5.334582e-7,6.988182e-7,0.00003858762,0.981702,0.002825795,0.01236782,0.002117579,0.00005183522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01390951,0.000002424062,0.9795756,0.003923215,0.0002060396,0.0001631804,0.000002051689,0.0002140486,0.002003914],"genre_scores_gemma":[0.7679076,0.000002840077,0.2212577,0.0006246606,0.0001718005,0.00004118332,0.00003400321,0.00000564199,0.009954564],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9678271,"threshold_uncertainty_score":0.132041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08974196501350201,"score_gpt":0.3223583861389037,"score_spread":0.2326164211254017,"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."}}