{"id":"W4406808467","doi":"10.5430/wjel.v15n3p354","title":"Evaluating the Performance of Large Language Models on Arabic Lexical Ambiguities: A Comparative Study with Traditional Machine Translation Systems","year":2025,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Prince Sattam bin Abdulaziz University","keywords":"Computer science; Arabic; Machine translation; Natural language processing; Artificial intelligence; Translation (biology); Linguistics; Philosophy; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.006822921,0.001551457,0.001195833,0.002298658,0.0008351798,0.002298669,0.00141295,0.001384788,0.002262607],"category_scores_gemma":[0.02718759,0.0003334587,0.0009099724,0.002650884,0.0007704881,0.003742226,0.001662243,0.001021488,0.001266148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00128511,"about_ca_system_score_gemma":0.001108038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008466288,"about_ca_topic_score_gemma":0.007962662,"domain_scores_codex":[0.9949921,0.002818173,0.0005281407,0.000703622,0.0008190733,0.0001388784],"domain_scores_gemma":[0.9717172,0.02335237,0.0007025052,0.001754094,0.002153338,0.0003204794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005095497,0.001722213,0.03905639,0.002229089,0.001674962,0.0008368194,0.002777483,0.3707091,0.01500958,0.003978396,0.008515954,0.5483944],"study_design_scores_gemma":[0.0002568126,0.002277978,0.01937834,0.0001253316,0.0005692019,0.0004736304,0.001572405,0.9524044,0.01304847,0.004276616,0.005483474,0.0001333589],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9309984,0.006276261,0.04790936,0.0008320798,0.0002930174,0.0002769963,0.001058101,0.002460504,0.009895455],"genre_scores_gemma":[0.9626979,0.0009747654,0.03303881,0.0001358457,0.00008747058,0.0001002266,0.001660779,0.0001808191,0.001123433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008466288,"threshold_uncertainty_score":0.03608352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04874044126831423,"score_gpt":0.3424484943563101,"score_spread":0.2937080530879959,"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."}}