{"id":"W7102329783","doi":"","title":"Low-Resource Dialect Adaptation of Large Language Models: A French Dialect Case-Study","year":2025,"lang":"","type":"article","venue":"arXiv (Cornell University)","topic":"Linguistic Variation and Morphology","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Adaptation (eye); Prestige; Benchmark (surveying); Resource (disambiguation); Variation (astronomy); Language model","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00304945,0.00127037,0.0006091993,0.001043638,0.001073236,0.001244965,0.001566595,0.001164257,0.001958674],"category_scores_gemma":[0.00927443,0.000298283,0.0008638181,0.001064364,0.000843928,0.0009632651,0.001350968,0.001574116,0.001004179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00229165,"about_ca_system_score_gemma":0.0014639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1709819,"about_ca_topic_score_gemma":0.2327817,"domain_scores_codex":[0.9982999,0.0009455206,0.00007591926,0.0004021385,0.0001446264,0.0001319305],"domain_scores_gemma":[0.9958969,0.002112007,0.00008775182,0.0009100651,0.0008144183,0.0001788282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001618098,0.0008440501,0.0746815,0.0006819865,0.0008261183,0.002165693,0.002184077,0.4576904,0.01444527,0.005610272,0.05479705,0.3844554],"study_design_scores_gemma":[0.0004800012,0.0005810662,0.03958613,0.0001219009,0.0002650612,0.001022639,0.001743427,0.8967544,0.01459533,0.007048378,0.03762465,0.0001771063],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9331732,0.001861561,0.04745021,0.001170513,0.0001849412,0.0002362814,0.003886495,0.005351028,0.006685657],"genre_scores_gemma":[0.9323848,0.0002685392,0.0529876,0.0004020033,0.00006549699,0.0001606545,0.009429158,0.000615991,0.003685723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1709819,"threshold_uncertainty_score":0.3399734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06044616730876317,"score_gpt":0.2366090311332262,"score_spread":0.1761628638244631,"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."}}