{"id":"W4376123230","doi":"10.1145/3539618.3591977","title":"SLIM: Sparsified Late Interaction for Multi-Vector Retrieval with Inverted Indexes","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Security token; Information retrieval; Inverted index; Vector space model; Artificial intelligence; Search engine indexing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001292256,0.001960849,0.001869607,0.001681888,0.0007541311,0.002823519,0.002885652,0.001372291,0.01762801],"category_scores_gemma":[0.007296755,0.000661304,0.001377143,0.002510779,0.0006851452,0.004917132,0.00407892,0.00188325,0.01457532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008936066,"about_ca_system_score_gemma":0.001993839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00475217,"about_ca_topic_score_gemma":0.009591118,"domain_scores_codex":[0.9986065,0.0002262913,0.0001271704,0.0002147529,0.0006579654,0.0001674231],"domain_scores_gemma":[0.9986635,0.0003767743,0.0001029665,0.0004675401,0.000311084,0.00007803617],"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":[0.0008366572,0.0002124942,0.001178288,0.0005976913,0.0001872319,0.0002907974,0.0002683142,0.04146796,0.03259798,0.02113406,0.05763724,0.8435913],"study_design_scores_gemma":[0.0001357928,0.0003366308,0.0006315805,0.00005781694,0.00006491638,0.0004675259,0.0001799146,0.8927528,0.04200348,0.02777654,0.0354753,0.0001176581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006421837,0.0006335533,0.9687248,0.0001646766,0.0001585134,0.0001841877,0.0009060794,0.02020091,0.002605341],"genre_scores_gemma":[0.09420338,0.0005911834,0.8837591,0.0004097472,0.0002110969,0.0005467748,0.007074995,0.002783365,0.01042033],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01762801,"threshold_uncertainty_score":0.05897158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1081651516638933,"score_gpt":0.3054581386246856,"score_spread":0.1972929869607923,"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."}}