{"id":"W4285272223","doi":"10.18653/v1/2022.acl-short.10","title":"Automatic Detection of Entity-Manipulated Text using Factual Knowledge","year":2022,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Compute Canada; Advanced Micro Devices","keywords":"Computer science; Convolutional neural network; Exploit; Focus (optics); Task (project management); Artificial intelligence; Information retrieval; Code (set theory); Graph; Knowledge graph; Natural language processing; Theoretical computer science; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001836782,0.00005544419,0.00008951063,0.0001166638,0.000146662,0.00002617651,0.0003527303,0.00001657921,0.0002480788],"category_scores_gemma":[0.00001379957,0.00005689566,0.00003699589,0.0003148293,0.000008411682,0.0001809306,0.000415865,0.00008574301,0.00001198567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008628897,"about_ca_system_score_gemma":0.0000448837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001594855,"about_ca_topic_score_gemma":0.00002225042,"domain_scores_codex":[0.9992939,0.0000683743,0.0001848678,0.0001710387,0.000165985,0.0001157825],"domain_scores_gemma":[0.9995808,0.00002901,0.00006099175,0.0002741989,0.00003032531,0.00002464916],"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":[0.000004379354,0.0003775998,0.0007591856,0.0001198957,0.00006765105,0.0000101835,0.008666249,0.06494404,0.2273131,0.02400641,0.00007071484,0.6736606],"study_design_scores_gemma":[0.00009981196,0.00003048117,0.0002977047,0.000003001747,0.000003404911,0.0000125764,0.00007191574,0.9884217,0.01029803,0.0005079847,0.0001902208,0.00006316146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4840239,0.00001762164,0.5150914,0.000009406222,0.0002356532,0.00004561037,2.3412e-7,0.0000802365,0.0004958959],"genre_scores_gemma":[0.9828686,2.148053e-7,0.016908,0.00001764694,0.00001272246,0.000003306851,3.682712e-7,0.000003937932,0.0001852516],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9234776,"threshold_uncertainty_score":0.2716289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04609297090021416,"score_gpt":0.27107876610736,"score_spread":0.2249857952071459,"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."}}