{"id":"W4385573082","doi":"10.18653/v1/2022.emnlp-main.92","title":"Generative Multi-hop Retrieval","year":2022,"lang":"en","type":"article","venue":"","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Samsung; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology","keywords":"Computer science; Encoder; Vector space model; Embedding; Information retrieval; Bottleneck; Generative grammar; Artificial intelligence; Data mining; Theoretical computer 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002141835,0.00006489859,0.0000625751,0.00005709202,0.0004332174,0.00005520842,0.0007725716,0.00001144716,0.0004055409],"category_scores_gemma":[0.00003863504,0.00006293809,0.00003263249,0.0004454062,0.00001466768,0.0001024127,0.0006694614,0.00022199,0.0001540873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005839419,"about_ca_system_score_gemma":0.00004397605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001657169,"about_ca_topic_score_gemma":0.0000036836,"domain_scores_codex":[0.9991357,0.0001116936,0.0001032564,0.0002887094,0.0002206683,0.0001399206],"domain_scores_gemma":[0.9993671,0.00005796374,0.00004069646,0.000449175,0.00003320592,0.00005186429],"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":[0.00001264992,0.0004973889,0.005786384,0.000004080814,0.00003485734,0.00001761267,0.003863894,0.04939875,0.03211284,0.8672436,0.008258816,0.03276909],"study_design_scores_gemma":[0.0002522407,0.00004576915,0.00982752,2.38688e-7,0.000001201818,0.00001131845,0.00003937913,0.9711537,0.001352635,0.0008670312,0.01632881,0.0001201236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02859271,0.00002344911,0.9599193,0.006010958,0.0001457552,0.0001527049,0.000002795735,0.0003802191,0.00477216],"genre_scores_gemma":[0.7016108,4.503825e-7,0.2948713,0.000909537,0.00002270347,0.00004064443,0.00000341478,0.000004977099,0.002536186],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.921755,"threshold_uncertainty_score":0.4440388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02634004319737722,"score_gpt":0.2973385893803788,"score_spread":0.2709985461830016,"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."}}