{"id":"W4411113474","doi":"10.18653/v1/2025.dravidianlangtech-1.10","title":"byteSizedLLM@DravidianLangTech 2025: Fake News Detection in Dravidian Languages Using Transliteration-Aware XLM-RoBERTa and Transformer Encoder-Decoder","year":2025,"lang":"en","type":"article","venue":"","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Atomic Energy of Canada Limited","keywords":"Encoder; Computer science; Transformer; Transliteration; Decoding methods; Natural language processing; Artificial intelligence; Algorithm; Electrical engineering; Operating system; Voltage; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009307199,0.001410378,0.0007195447,0.001394919,0.0006048088,0.001240877,0.0008527653,0.0008758027,0.005749797],"category_scores_gemma":[0.003996928,0.000345588,0.000519732,0.0005361716,0.0004735028,0.002223827,0.001769216,0.001180629,0.008253265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006589429,"about_ca_system_score_gemma":0.001123662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00664498,"about_ca_topic_score_gemma":0.01110438,"domain_scores_codex":[0.9991317,0.000179931,0.00005624464,0.0003818818,0.0001533277,0.00009696224],"domain_scores_gemma":[0.9988016,0.0003336365,0.00009330358,0.0004259535,0.0002623708,0.00008307025],"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":[0.001379509,0.0003706308,0.01083215,0.0006317875,0.0001337833,0.001128438,0.0006289539,0.00910149,0.07419904,0.002985673,0.07282183,0.8257867],"study_design_scores_gemma":[0.0001893321,0.0007294762,0.00965238,0.0001218055,0.0001554955,0.001920687,0.000816225,0.6911333,0.2283928,0.006498121,0.06022177,0.0001686463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4121482,0.003014782,0.298327,0.001541432,0.001081758,0.0006597604,0.02021916,0.2439678,0.01904015],"genre_scores_gemma":[0.7125427,0.0005637289,0.229651,0.000644332,0.0001801429,0.0002963633,0.03764759,0.002116951,0.01635718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00664498,"threshold_uncertainty_score":0.01923496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02235026604751183,"score_gpt":0.3590581183390888,"score_spread":0.336707852291577,"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."}}