{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001308903,0.0008929555,0.0004809526,0.00506356,0.0006898029,0.001937784,0.001411271,0.001368967,0.002650958],"category_scores_gemma":[0.007827323,0.000432623,0.0005363459,0.002320077,0.0006795067,0.005340514,0.001135376,0.001238071,0.001496309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000793085,"about_ca_system_score_gemma":0.0005642145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001849266,"about_ca_topic_score_gemma":0.003224625,"domain_scores_codex":[0.9985764,0.0002585862,0.0001144225,0.00049307,0.000456952,0.0001005107],"domain_scores_gemma":[0.9924273,0.003990381,0.001401945,0.0007928706,0.001198222,0.0001892716],"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":[0.001225108,0.0003911717,0.05030691,0.002247296,0.0004437012,0.005699374,0.002341687,0.02198922,0.115869,0.03459565,0.05328356,0.7116074],"study_design_scores_gemma":[0.00006794419,0.000183699,0.03420946,0.000306929,0.0003735675,0.003972223,0.0009158948,0.6182604,0.184186,0.02989711,0.1274844,0.0001425055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4188233,0.01017546,0.5174246,0.001995047,0.001205636,0.0005373782,0.01438549,0.01212035,0.02333261],"genre_scores_gemma":[0.7834646,0.001717618,0.1845458,0.0003799255,0.0006475045,0.0001532703,0.02002554,0.0004059336,0.008659798],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00506356,"threshold_uncertainty_score":0.008868277,"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."}}