{"id":"W4392669927","doi":"10.18653/v1/2023.findings-ijcnlp.4","title":"PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Supercomputing Center, Korea Institute of Science and Technology Information; Institute for Information and Communications Technology Promotion; Samsung; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology","keywords":"Relation (database); Computer science; Calibration; Prism; Relationship extraction; Resource (disambiguation); Code (set theory); Data mining; Key (lock); Source code; Information retrieval; Perspective (graphical); Artificial intelligence; Machine learning; Statistics; Optics; Mathematics; Programming language","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":[0.0003596355,0.0001284759,0.00009974866,0.0001858943,0.0002288464,0.000208086,0.0002379501,0.00009633932,0.00003245191],"category_scores_gemma":[0.00003797595,0.0001127794,0.00003063159,0.0006824268,0.00001239987,0.00195646,0.00008911379,0.0001732435,0.0001114973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001003229,"about_ca_system_score_gemma":0.00007831803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006593688,"about_ca_topic_score_gemma":0.00007285414,"domain_scores_codex":[0.9985331,0.00006099603,0.0003044086,0.0004358643,0.000433561,0.0002321158],"domain_scores_gemma":[0.9991817,0.000115874,0.00013973,0.0004434567,0.00005105762,0.00006817331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007904012,0.0001260224,0.02981599,0.0001984786,0.0001092737,0.00009549142,0.01352631,0.549414,0.02999932,0.2072522,0.005635048,0.1637488],"study_design_scores_gemma":[0.000278314,0.00003596939,0.01632007,0.00008675113,0.000006350479,0.0000124491,0.0001207538,0.9738016,0.006393996,0.002306213,0.0004412078,0.0001963329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1230001,0.000006540874,0.8734442,0.001122718,0.0001695044,0.0002154079,3.476399e-7,0.0006736706,0.001367484],"genre_scores_gemma":[0.9461379,0.000003537052,0.04919975,0.0001460205,0.000103566,0.00002455,0.0000377628,0.00001584981,0.004331036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8242444,"threshold_uncertainty_score":0.459901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02968541768941629,"score_gpt":0.2561387947892135,"score_spread":0.2264533770997972,"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."}}