{"id":"W4385572399","doi":"10.18653/v1/2023.acl-long.805","title":"Knowledge Unlearning for Mitigating Privacy Risks in Language Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Interpreting and Communication in Healthcare","field":"Health Professions","cited_by":58,"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","keywords":"Computer science; Volume (thermodynamics); Computational linguistics; Knowledge management; Natural language processing","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":[0.01080807,0.001139521,0.002019895,0.001736989,0.001813675,0.003137106,0.002340468,0.00241414,0.002468427],"category_scores_gemma":[0.05834386,0.0008225071,0.001453619,0.001246301,0.002745806,0.01172168,0.006247106,0.005652485,0.0006094066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00156877,"about_ca_system_score_gemma":0.002918645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00375245,"about_ca_topic_score_gemma":0.005031139,"domain_scores_codex":[0.9924896,0.003926731,0.0004327432,0.001263467,0.001347813,0.0005395153],"domain_scores_gemma":[0.9386656,0.04907195,0.002009914,0.007140262,0.002418867,0.0006933854],"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":[0.001512866,0.0006014152,0.009893539,0.000470636,0.0004883235,0.0004303882,0.001058544,0.3962681,0.003531892,0.1113751,0.009396059,0.4649732],"study_design_scores_gemma":[0.00003783356,0.0000710665,0.0001961691,0.00004837094,0.00007810989,0.00008467646,0.00009769083,0.8678461,0.002406926,0.1280015,0.001110746,0.00002089069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04459842,0.00184065,0.9456263,0.003790645,0.0001536807,0.0001243056,0.0002382147,0.001085854,0.002542017],"genre_scores_gemma":[0.8320137,0.001126068,0.1619319,0.001068858,0.0002652289,0.0001988851,0.0006508601,0.0002286972,0.002515733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01080807,"threshold_uncertainty_score":0.05715919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2917297810826175,"score_gpt":0.5566346360605682,"score_spread":0.2649048549779506,"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."}}