{"id":97201,"date":"2026-09-16T08:16:36","date_gmt":"2026-09-16T05:16:36","guid":{"rendered":"https:\/\/twodots.gr\/?p=97201"},"modified":"2026-09-16T08:16:36","modified_gmt":"2026-09-16T05:16:36","slug":"hate-speech-roman-urdu-lora-prompting","status":"publish","type":"post","link":"https:\/\/twodots.gr\/bg\/hate-speech-roman-urdu-lora-prompting\/","title":{"rendered":"Hate speech \u03c3\u03b5 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b5\u03c2 \u03bc\u03b5 \u03bb\u03af\u03b3\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1: \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf LoRA \u03be\u03b5\u03c0\u03ad\u03c1\u03b1\u03c3\u03b5 \u03c4\u03bf \u03b1\u03c0\u03bb\u03cc prompting"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>\u0391\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03c0\u03c1\u03ce\u03c4\u03b1:<\/strong> \u03c3\u03c4\u03bf \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03c4\u03b7\u03c2 Toneema Zubair \u03b3\u03b9\u03b1 \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7 toxic\/non-toxic \u03c3\u03c7\u03bf\u03bb\u03af\u03c9\u03bd \u03c3\u03c4\u03b7 Roman Urdu, \u03c4\u03bf LoRA \u03ad\u03b4\u03c9\u03c3\u03b5 \u03c4\u03b7\u03bd \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03b7 \u03ba\u03b1\u03b9 \u03c0\u03b9\u03bf \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ae \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03ae \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7. \u03a4\u03bf Mistral-7B-v0.3 \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 macro F1 0,9387 \u03bc\u03b5\u03c4\u03ac \u03c4\u03b7\u03bd \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae, \u03b5\u03bd\u03ce \u03c4\u03bf GPT-3.5 \u03bc\u03b5 few-shot prompting \u03b5\u03af\u03c7\u03b5 \u03bc\u03ad\u03c3\u03bf F1 0,8565 \u03ba\u03b1\u03b9 \u03b7 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 mixed prompt-tuning \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 0,6852.<\/p>\n<p>\u03a4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf LoRA \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03ac\u03bd\u03c4\u03b1 \u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03bb\u03cd\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1 \u03ae moderation policy. \u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03bc\u03b7 \u03b4\u03b7\u03bc\u03cc\u03c3\u03b9\u03bf corpus, binary labels \u03ba\u03b1\u03b9 Roman Urdu, \u03b5\u03bd\u03ce \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf\u03b9 \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2 \u03ad\u03c7\u03bf\u03c5\u03bd \u03b1\u03c3\u03cd\u03bc\u03b2\u03b1\u03c4\u03b1 \u03bc\u03b5\u03b3\u03ad\u03b8\u03b7 test set \u03ba\u03b1\u03b9 confusion counts. \u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7, \u03c4\u03bf \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b9\u03bf \u03b1\u03c5\u03c3\u03c4\u03b7\u03c1\u03cc: \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7, \u03b1\u03bd\u03c4\u03b9\u03c0\u03c1\u03bf\u03c3\u03c9\u03c0\u03b5\u03c5\u03c4\u03b9\u03ba\u03ac labels, slice metrics, appeals \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf\u03c2 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b7\u03c2 \u03ba\u03cd\u03c1\u03c9\u03c3\u03b7\u03c2.<\/p>\n<\/div>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">\u0421\u044a\u0434\u044a\u0440\u0436\u0430\u043d\u0438\u0435<\/div>\n<ul>\n<li><a href=\"#roman-urdu-dyskolo-test\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 Roman Urdu \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03cd\u03c3\u03ba\u03bf\u03bb\u03bf \u03c4\u03b5\u03c3\u03c4 \u03b3\u03b9\u03b1 \u03c4\u03bf content moderation<\/a><\/li>\n<li><a href=\"#purutt-labels-imbalance\">\u03a4\u03bf PURUTT, \u03c4\u03b1 labels \u03ba\u03b1\u03b9 \u03b7 \u03b1\u03bd\u03b9\u03c3\u03bf\u03c1\u03c1\u03bf\u03c0\u03af\u03b1 \u03c4\u03c9\u03bd \u03ba\u03bb\u03ac\u03c3\u03b5\u03c9\u03bd<\/a><\/li>\n<li><a href=\"#tessera-peiramata\">\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b1 \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1 \u03bc\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03b2\u03b1\u03b8\u03bc\u03cc \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae\u03c2<\/a><\/li>\n<li><a href=\"#zero-shot-false-positives\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03c4\u03bf zero-shot baseline<\/a><\/li>\n<li><a href=\"#lora-apotelesmata\">\u03a0\u03ce\u03c2 \u03c4\u03bf LoRA \u03ac\u03bb\u03bb\u03b1\u03be\u03b5 \u03c4\u03b7\u03bd \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7<\/a><\/li>\n<li><a href=\"#prompt-tuning-few-shot\">Prompt tuning \u03ba\u03b1\u03b9 few-shot prompting \u03c3\u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7<\/a><\/li>\n<li><a href=\"#ti-apodeiknyei-sygkrisi\">\u03a4\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03c4\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7<\/a><\/li>\n<li><a href=\"#asynepeies-pinakon\">\u039f\u03b9 \u03b1\u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03b9\u03b5\u03c2 \u03c4\u03c9\u03bd \u03c0\u03b9\u03bd\u03ac\u03ba\u03c9\u03bd \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03b3\u03bd\u03bf\u03b7\u03b8\u03bf\u03cd\u03bd<\/a><\/li>\n<li><a href=\"#moderation-paragogi\">\u0391\u03c0\u03cc \u03c4\u03bf classifier \u03c3\u03b5 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ae \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1 moderation<\/a><\/li>\n<li><a href=\"#dataset-governance\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 \u03b4\u03b9\u03b1\u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 dataset \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2<\/a><\/li>\n<li><a href=\"#pilot-epta-vimata\">\u0388\u03bd\u03b1 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf pilot \u03c3\u03b5 \u03b5\u03c0\u03c4\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1<\/a><\/li>\n<li><a href=\"#teliko-symperasma\">\u03a4\u03bf \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b3\u03b9\u03b1 low-resource \u03b3\u03bb\u03ce\u03c3\u03c3\u03b5\u03c2<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"roman-urdu-dyskolo-test\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 Roman Urdu \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03cd\u03c3\u03ba\u03bf\u03bb\u03bf \u03c4\u03b5\u03c3\u03c4 \u03b3\u03b9\u03b1 \u03c4\u03bf content moderation<\/h2>\n<p>\u0397 Roman Urdu \u03b1\u03c0\u03bf\u03b4\u03af\u03b4\u03b5\u03b9 \u03c4\u03b7\u03bd Urdu \u03bc\u03b5 \u03bb\u03b1\u03c4\u03b9\u03bd\u03b9\u03ba\u03bf\u03cd\u03c2 \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03ae\u03c1\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03b5\u03c5\u03c1\u03ad\u03c9\u03c2 \u03c3\u03b5 \u03c8\u03b7\u03c6\u03b9\u03b1\u03ba\u03ad\u03c2 \u03c3\u03c5\u03bd\u03bf\u03bc\u03b9\u03bb\u03af\u03b5\u03c2 \u03c3\u03c4\u03bf \u03a0\u03b1\u03ba\u03b9\u03c3\u03c4\u03ac\u03bd, \u03c3\u03c4\u03b7\u03bd \u0399\u03bd\u03b4\u03af\u03b1 \u03ba\u03b1\u03b9 \u03c3\u03b5 \u03ba\u03bf\u03b9\u03bd\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03bf\u03bc\u03b9\u03bb\u03b7\u03c4\u03ce\u03bd Urdu. \u0394\u03b5\u03bd \u03b4\u03b9\u03b1\u03b8\u03ad\u03c4\u03b5\u03b9 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ac \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03ae \u03bf\u03c1\u03b8\u03bf\u03b3\u03c1\u03b1\u03c6\u03af\u03b1. \u0397 \u03af\u03b4\u03b9\u03b1 \u03bb\u03ad\u03be\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c9\u03c2 <em>acha<\/em>, <em>achaa<\/em>, <em>achha<\/em> \u03ae <em>achaw<\/em>, \u03b5\u03bd\u03ce \u03bc\u03b9\u03b1 \u03b3\u03c1\u03b1\u03c6\u03ae \u03cc\u03c0\u03c9\u03c2 <em>badal<\/em> \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03c0\u03ad\u03bc\u03c0\u03b5\u03b9 \u03c3\u03b5 \u00ab\u03c3\u03cd\u03bd\u03bd\u03b5\u03c6\u03bf\u00bb \u03ae \u00ab\u03b1\u03bb\u03bb\u03b1\u03b3\u03ae\u00bb \u03b1\u03bd\u03ac\u03bb\u03bf\u03b3\u03b1 \u03bc\u03b5 \u03c4\u03b1 \u03c3\u03c5\u03bc\u03c6\u03c1\u03b1\u03b6\u03cc\u03bc\u03b5\u03bd\u03b1.<\/p>\n<p>\u0397 \u03b1\u03c3\u03c4\u03ac\u03b8\u03b5\u03b9\u03b1 \u03b1\u03c5\u03c4\u03ae \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03cc \u03bf\u03c1\u03b8\u03bf\u03b3\u03c1\u03b1\u03c6\u03b9\u03ba\u03cc \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1. \u039c\u03b5\u03c4\u03b1\u03b2\u03ac\u03bb\u03bb\u03b5\u03b9 \u03c4\u03bf tokenization, \u03b1\u03c0\u03bf\u03b4\u03c5\u03bd\u03b1\u03bc\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b1 \u03bb\u03b5\u03be\u03b9\u03ba\u03ac \u03bc\u03bf\u03c4\u03af\u03b2\u03b1 \u03ba\u03b1\u03b9 \u03ba\u03ac\u03bd\u03b5\u03b9 \u03c0\u03b9\u03bf \u03b4\u03cd\u03c3\u03ba\u03bf\u03bb\u03b7 \u03c4\u03b7 \u03b3\u03b5\u03bd\u03af\u03ba\u03b5\u03c5\u03c3\u03b7 \u03c3\u03b5 \u03bd\u03ad\u03bf\u03c5\u03c2 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b5\u03c2, \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ad\u03c2 \u03c0\u03b5\u03c1\u03b9\u03cc\u03b4\u03bf\u03c5\u03c2. \u03a4\u03bf \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1 \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b5\u03bd\u03c4\u03bf\u03bd\u03cc\u03c4\u03b5\u03c1\u03bf \u03c3\u03b5 \u03c3\u03cd\u03bd\u03c4\u03bf\u03bc\u03b1 \u03c3\u03c7\u03cc\u03bb\u03b9\u03b1, \u03c3\u03b5 code-switching \u03ba\u03b1\u03b9 \u03c3\u03b5 \u03b1\u03c1\u03b3\u03ba\u03cc \u03c0\u03bf\u03c5 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b1.<\/p>\n<p>\u0397 \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03b4\u03b5\u03bd \u03c4\u03b1\u03c5\u03c4\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 sentiment analysis. \u03a4\u03bf \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2 \u03b3\u03b9\u03b1 \u03bc\u03b9\u03b1 \u00ab\u03c0\u03bf\u03bb\u03cd \u03ba\u03b1\u03ba\u03ae \u03bc\u03c0\u03b1\u03c4\u03b1\u03c1\u03af\u03b1 \u03ba\u03b9\u03bd\u03b7\u03c4\u03bf\u03cd\u00bb \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03cc \u03c3\u03c7\u03cc\u03bb\u03b9\u03bf \u03b3\u03b9\u03b1 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd, \u03cc\u03c7\u03b9 \u03c3\u03c4\u03bf\u03c7\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03bf\u03c5 \u03ae \u03bf\u03bc\u03ac\u03b4\u03b1\u03c2. \u03a3\u03b5 e-commerce reviews, social media \u03ae help desk, \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03bf\u03c5 \u03c3\u03c5\u03b3\u03c7\u03ad\u03b5\u03b9 \u03c4\u03b7\u03bd \u03ba\u03c1\u03b9\u03c4\u03b9\u03ba\u03ae \u03bc\u03b5 \u03c4\u03b7\u03bd \u03c4\u03bf\u03be\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b1\u03c6\u03b1\u03b9\u03c1\u03b5\u03af \u03bd\u03cc\u03bc\u03b9\u03bc\u03bf feedback. \u0388\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03ba\u03b1\u03c4\u03b1\u03bb\u03b1\u03b2\u03b1\u03af\u03bd\u03b5\u03b9 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ad\u03c2 \u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c0\u03bf\u03c7\u03c1\u03ce\u03c3\u03b5\u03b9\u03c2 \u03b1\u03c6\u03ae\u03bd\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b5\u03c0\u03b9\u03b2\u03bb\u03b1\u03b2\u03ad\u03c2 \u03c0\u03b5\u03c1\u03b9\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf \u03bd\u03b1 \u03c0\u03b5\u03c1\u03ac\u03c3\u03b5\u03b9.<\/p>\n<p>\u0391\u03c5\u03c4\u03ae \u03b7 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03ac\u03bc\u03b5\u03c3\u03b1 \u03bc\u03b5 \u03c4\u03b7\u03bd <a href=\"https:\/\/twodots.gr\/oi-kalyteres-lyseis-help-desk-gia-epicheiriseis\/\">\u03bf\u03c1\u03b3\u03ac\u03bd\u03c9\u03c3\u03b7 \u03b5\u03bd\u03cc\u03c2 help desk \u03b3\u03b9\u03b1 e-commerce<\/a>: \u03c4\u03bf AI \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03b9\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03b5\u03b9, \u03b1\u03bb\u03bb\u03ac \u03b7 \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ae \u03ba\u03bb\u03b9\u03bc\u03ac\u03ba\u03c9\u03c3\u03b7\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03c3\u03b1\u03c6\u03ae\u03c2 \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 \u03bc\u03ae\u03bd\u03c5\u03bc\u03b1 \u03b1\u03c6\u03bf\u03c1\u03ac \u03ba\u03b1\u03c4\u03b1\u03b3\u03b3\u03b5\u03bb\u03af\u03b1, \u03c0\u03b1\u03c1\u03b5\u03bd\u03cc\u03c7\u03bb\u03b7\u03c3\u03b7 \u03ae \u03ba\u03cd\u03c1\u03c9\u03c3\u03b7 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b7.<\/p>\n<h2 id=\"purutt-labels-imbalance\">\u03a4\u03bf PURUTT, \u03c4\u03b1 labels \u03ba\u03b1\u03b9 \u03b7 \u03b1\u03bd\u03b9\u03c3\u03bf\u03c1\u03c1\u03bf\u03c0\u03af\u03b1 \u03c4\u03c9\u03bd \u03ba\u03bb\u03ac\u03c3\u03b5\u03c9\u03bd<\/h2>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c4\u03bf PURUTT, \u03ad\u03bd\u03b1 \u03c0\u03b1\u03c1\u03ac\u03bb\u03bb\u03b7\u03bb\u03bf corpus Urdu \u03ba\u03b1\u03b9 Roman Urdu \u03bc\u03b5 72.771 \u03b5\u03c0\u03b9\u03c3\u03b7\u03bc\u03b1\u03c3\u03bc\u03ad\u03bd\u03b1 \u03c3\u03c7\u03cc\u03bb\u03b9\u03b1. \u0393\u03b9\u03b1 \u03c4\u03b1 \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1 \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b7 Roman Urdu \u03b5\u03ba\u03b4\u03bf\u03c7\u03ae. \u0397 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03c4\u03bf\u03bd\u03b1 \u03ac\u03bd\u03b9\u03c3\u03b7: 13.097 \u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 toxic \u03ba\u03b1\u03b9 59.674 non-toxic. \u0397 \u03b5\u03c1\u03b5\u03c5\u03bd\u03ae\u03c4\u03c1\u03b9\u03b1 \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03b6\u03b5\u03b9 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03c1\u03bf\u03c6\u03b1 \u03b2\u03ac\u03c1\u03b7 \u03c3\u03c5\u03c7\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf training loss \u03bd\u03b1 \u03c4\u03b9\u03bc\u03c9\u03c1\u03b5\u03af \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03c4\u03b1 \u03bb\u03ac\u03b8\u03b7 \u03c3\u03c4\u03b7 \u03bc\u03b5\u03b9\u03bf\u03c8\u03b7\u03c6\u03b9\u03ba\u03ae \u03ba\u03bb\u03ac\u03c3\u03b7.<\/p>\n<p>\u0397 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae F1, precision \u03ba\u03b1\u03b9 recall \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03c5\u03c3\u03b9\u03ce\u03b4\u03b7\u03c2. \u0388\u03bd\u03b1 \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03b5\u03b9 \u03c3\u03c5\u03c7\u03bd\u03ac \u03c4\u03b7\u03bd \u03c0\u03bb\u03b5\u03b9\u03bf\u03c8\u03b7\u03c6\u03b9\u03ba\u03ae non-toxic \u03ba\u03bb\u03ac\u03c3\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03b9 \u03c5\u03c8\u03b7\u03bb\u03cc accuracy \u03ba\u03b1\u03b9 \u03c0\u03b1\u03c1&#8217; \u03cc\u03bb\u03b1 \u03b1\u03c5\u03c4\u03ac \u03bd\u03b1 \u03c7\u03ac\u03bd\u03b5\u03b9 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b2\u03bb\u03b1\u03b2\u03b5\u03c1\u03bf\u03cd \u03c0\u03b5\u03c1\u03b9\u03b5\u03c7\u03bf\u03bc\u03ad\u03bd\u03bf\u03c5. \u03a3\u03c4\u03b7\u03bd \u03b1\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b7 \u03ba\u03b1\u03c4\u03b5\u03cd\u03b8\u03c5\u03bd\u03c3\u03b7, \u03c5\u03c0\u03b5\u03c1\u03b2\u03bf\u03bb\u03b9\u03ba\u03cc recall \u03c7\u03c9\u03c1\u03af\u03c2 \u03b5\u03c0\u03b1\u03c1\u03ba\u03ad\u03c2 precision \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af false positives, \u03c6\u03cc\u03c1\u03c4\u03bf \u03b3\u03b9\u03b1 reviewers \u03ba\u03b1\u03b9 \u03b1\u03b4\u03b9\u03ba\u03b1\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c4\u03b7 \u03b1\u03c0\u03cc\u03ba\u03c1\u03c5\u03c8\u03b7 \u03c3\u03c7\u03bf\u03bb\u03af\u03c9\u03bd.<\/p>\n<aside class=\"td-article-note\"><strong>\u03a4\u03bf label \u03b8\u03ad\u03bb\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf\u03bd \u03c4\u03af\u03c4\u03bb\u03bf:<\/strong> \u03c4\u03bf paper \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c4\u03bf\u03bd \u03cc\u03c1\u03bf hate speech, \u03b1\u03bb\u03bb\u03ac \u03c4\u03bf task \u03ba\u03b1\u03b9 \u03c4\u03bf PURUTT \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c9\u03c2 binary toxic\/non-toxic classification. \u0397 \u03c4\u03bf\u03be\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1, \u03b7 \u03c0\u03c1\u03bf\u03c3\u03b2\u03bf\u03bb\u03ae \u03ba\u03b1\u03b9 \u03b7 \u03c3\u03c4\u03bf\u03c7\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03c0\u03c1\u03bf\u03c3\u03c4\u03b1\u03c4\u03b5\u03c5\u03cc\u03bc\u03b5\u03bd\u03b7\u03c2 \u03bf\u03bc\u03ac\u03b4\u03b1\u03c2 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c3\u03b7\u03bc\u03b5\u03c2 \u03ad\u03bd\u03bd\u03bf\u03b9\u03b5\u03c2. \u039c\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03ae policy taxonomy \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c4\u03b9\u03c2 \u03b4\u03b9\u03b1\u03c7\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03c0\u03c1\u03b9\u03bd \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b7\u03b8\u03bf\u03cd\u03bd labels \u03ae thresholds.<\/aside>\n<p>\u03a4\u03bf PURUTT \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03b7\u03bc\u03cc\u03c3\u03b9\u03b1 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03bf \u03c3\u03cd\u03bc\u03c6\u03c9\u03bd\u03b1 \u03bc\u03b5 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c4\u03c1\u03b9\u03b2\u03ae \u03ba\u03b1\u03b9 \u03c0\u03b1\u03c1\u03b1\u03c7\u03c9\u03c1\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b3\u03b9\u03b1 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ae \u03c7\u03c1\u03ae\u03c3\u03b7 \u03ba\u03b1\u03c4\u03cc\u03c0\u03b9\u03bd \u03b1\u03b9\u03c4\u03ae\u03bc\u03b1\u03c4\u03bf\u03c2. \u0391\u03c5\u03c4\u03cc \u03b4\u03c5\u03c3\u03ba\u03bf\u03bb\u03b5\u03cd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b7 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03c4\u03c9\u03bd splits \u03ba\u03b1\u03b9 \u03c4\u03c9\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03ac\u03c4\u03c9\u03bd. \u0393\u03b9\u03b1 \u03ad\u03bd\u03b1\u03bd \u03bf\u03c1\u03b3\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc, \u03c4\u03bf \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 dataset version, \u03b4\u03b9\u03ba\u03b1\u03b9\u03ce\u03bc\u03b1\u03c4\u03b1 \u03c7\u03c1\u03ae\u03c3\u03b7\u03c2, annotation guide \u03ba\u03b1\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ad\u03c2 holdout \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03b5\u03bb\u03ad\u03b3\u03be\u03b9\u03bc\u03b1 artifacts, \u03cc\u03c7\u03b9 \u03ac\u03c4\u03c5\u03c0\u03b5\u03c2 \u03b3\u03bd\u03ce\u03c3\u03b5\u03b9\u03c2 \u03c4\u03b7\u03c2 \u03bf\u03bc\u03ac\u03b4\u03b1\u03c2.<\/p>\n<h2 id=\"tessera-peiramata\">\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b1 \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1 \u03bc\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03b2\u03b1\u03b8\u03bc\u03cc \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae\u03c2<\/h2>\n<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03bf\u03c1\u03b3\u03b1\u03bd\u03ce\u03bd\u03b5\u03b9 \u03c4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b9\u03c2 \u03b3\u03c1\u03b1\u03bc\u03bc\u03ad\u03c2 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2. \u0397 \u03c0\u03c1\u03ce\u03c4\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 direct inference \u03c7\u03c9\u03c1\u03af\u03c2 task-specific fine-tuning. \u0397 \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03b7 \u03ba\u03c1\u03b1\u03c4\u03ac \u03c0\u03b1\u03b3\u03c9\u03bc\u03ad\u03bd\u03bf \u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03ba\u03ac\u03b8\u03b5 backbone \u03ba\u03b1\u03b9 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03b5\u03b9 LoRA adapters. \u0397 \u03c4\u03c1\u03af\u03c4\u03b7 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af prompt tuning \u03c3\u03c4\u03bf BERT-base-multilingual-uncased \u03bc\u03b5 prefix, cloze \u03ba\u03b1\u03b9 mixed templates. \u0397 \u03c4\u03ad\u03c4\u03b1\u03c1\u03c4\u03b7 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 zero-shot \u03ba\u03b1\u03b9 few-shot instructions \u03c3\u03c4\u03bf GPT-3.5 \u03bc\u03ad\u03c3\u03c9 Azure OpenAI \u03c7\u03c9\u03c1\u03af\u03c2 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c4\u03c9\u03bd weights.<\/p>\n<p>\u039f\u03b9 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ad\u03c2 \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf\u03cd\u03bd \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2. \u03a4\u03bf zero-shot \u03be\u03b5\u03ba\u03b9\u03bd\u03ac \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b1, \u03b1\u03bb\u03bb\u03ac \u03b2\u03b1\u03c3\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b9\u03c2 \u03ae\u03b4\u03b7 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b5\u03c2 \u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03ad\u03c2 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03c3\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2. \u03a4\u03bf few-shot \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 prompt \u03ba\u03b1\u03b9 \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c4\u03bf token cost. \u03a4\u03bf prompt tuning \u03bc\u03b1\u03b8\u03b1\u03af\u03bd\u03b5\u03b9 task-specific soft tokens \u03ae verbalizers. \u03a4\u03bf LoRA \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 training pipeline \u03ba\u03b1\u03b9 versioned adapters, \u03b1\u03bb\u03bb\u03ac \u03b5\u03bd\u03b7\u03bc\u03b5\u03c1\u03ce\u03bd\u03b5\u03b9 \u03c0\u03bf\u03bb\u03cd \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf full fine-tuning.<\/p>\n<p>\u03a3\u03c4\u03bf LoRA \u03c3\u03ba\u03ad\u03bb\u03bf\u03c2 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 Mistral, Llama, Gemma, DeepSeek, Falcon \u03ba\u03b1\u03b9 multilingual BERT. \u0397 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7 \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 NVIDIA A100 \u03b3\u03b9\u03b1 \u03b4\u03cd\u03bf epochs, \u03bc\u03b5 60\/20\/20 split \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 \u03c3\u03b5 batch size \u03ba\u03b1\u03b9 learning rate. \u03a4\u03bf paper \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 6,6 \u03ad\u03c9\u03c2 7,2 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1 trainable parameters \u03bc\u03b5\u03c4\u03ac \u03c4\u03b7\u03bd \u03c0\u03c1\u03bf\u03c3\u03b8\u03ae\u03ba\u03b7 adapters \u03b3\u03b9\u03b1 \u03c4\u03b1 \u03b5\u03be\u03b5\u03c4\u03b1\u03b6\u03cc\u03bc\u03b5\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1.<\/p>\n<p>\u0397 \u03ba\u03bb\u03b9\u03bc\u03ac\u03ba\u03c9\u03c3\u03b7 \u03b1\u03c5\u03c4\u03ae \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b7 Roman Urdu. \u039c\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03be\u03b5\u03ba\u03b9\u03bd\u03ae\u03c3\u03b5\u03b9 \u03bc\u03b5 prompt baseline, \u03bd\u03b1 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 few-shot \u03ba\u03b1\u03b9 adapter tuning \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03ba\u03bb\u03b5\u03b9\u03b4\u03c9\u03bc\u03ad\u03bd\u03bf holdout \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b9\u03bb\u03ad\u03be\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b1\u03bd \u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03c9\u03bd false positives, \u03c4\u03c9\u03bd false negatives \u03ba\u03b1\u03b9 \u03c4\u03b7\u03c2 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7\u03c2 \u03b1\u03bd\u03b1\u03b8\u03b5\u03ce\u03c1\u03b7\u03c3\u03b7\u03c2.<\/p>\n<h2 id=\"zero-shot-false-positives\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03c4\u03bf zero-shot baseline<\/h2>\n<p>\u03a3\u03c4\u03b7\u03bd direct-inference \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7, \u03c4\u03bf Mistral-7B-v0.3 \u03ad\u03c7\u03b5\u03b9 \u03c4\u03b7\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03b7 baseline \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7, \u03bc\u03b5 macro F1 0,56 \u03ba\u03b1\u03b9 accuracy 0,7262. \u03a4\u03bf DeepSeek-R1-7B \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b5\u03af \u03bc\u03b5 F1 0,51 \u03ba\u03b1\u03b9 accuracy 0,7402. \u03a4\u03b1 Falcon, Llama, Gemma \u03ba\u03b1\u03b9 multilingual BERT \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b1 \u03c3\u03c4\u03bf\u03bd \u03b1\u03bd\u03b1\u03bb\u03c5\u03c4\u03b9\u03ba\u03cc \u03c0\u03af\u03bd\u03b1\u03ba\u03b1, \u03bc\u03b5 F1 \u03b1\u03c0\u03cc 0,32 \u03ad\u03c9\u03c2 0,15.<\/p>\n<p>\u039f \u03c0\u03af\u03bd\u03b1\u03ba\u03b1\u03c2 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae\u03c2 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03c9\u03bd \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03c4\u03bf \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03cc \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1. \u03a3\u03c4\u03bf baseline subset \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd\u03c4\u03b1\u03b9 4.190 non-toxic \u03ba\u03b1\u03b9 774 toxic \u03c3\u03c7\u03cc\u03bb\u03b9\u03b1. \u03a4\u03bf Mistral \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03b5\u03b9 1.054 toxic, \u03b5\u03bd\u03ce \u03c4\u03bf Llama 3.021 \u03ba\u03b1\u03b9 \u03c4\u03bf multilingual BERT 3.159. \u0391\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03bf\u03c5\u03bc\u03b5 \u03c4\u03bf\u03c5\u03c2 \u03c0\u03c1\u03bf\u03b2\u03bb\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03bf\u03cd\u03c2 confusion counts \u03c4\u03bf\u03c5 \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf\u03c5 \u03c0\u03af\u03bd\u03b1\u03ba\u03b1, \u03b7 \u03c0\u03c1\u03bf\u03b2\u03bb\u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03b7 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03ad\u03bd\u03c4\u03bf\u03bd\u03b7 \u03c4\u03ac\u03c3\u03b7 \u03c5\u03c0\u03b5\u03c1\u03c3\u03ae\u03bc\u03b1\u03bd\u03c3\u03b7\u03c2 \u03b3\u03b9\u03b1 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1.<\/p>\n<p>\u03a3\u03b5 \u03bc\u03b9\u03b1 \u03c0\u03bb\u03b1\u03c4\u03c6\u03cc\u03c1\u03bc\u03b1 \u03c3\u03c7\u03bf\u03bb\u03af\u03c9\u03bd, \u03c4\u03ad\u03c4\u03bf\u03b9\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03bc\u03b5\u03c4\u03b1\u03c6\u03c1\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 review queue, \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b1\u03bc\u03c6\u03b9\u03c3\u03b2\u03b7\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03c0\u03b9\u03b8\u03b1\u03bd\u03ae \u03c3\u03af\u03b3\u03b1\u03c3\u03b7 \u03bd\u03cc\u03bc\u03b9\u03bc\u03c9\u03bd \u03c3\u03c7\u03bf\u03bb\u03af\u03c9\u03bd. \u0394\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03bb\u03bf\u03b9\u03c0\u03cc\u03bd \u03bd\u03b1 \u03b4\u03b7\u03bb\u03c9\u03b8\u03b5\u03af \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u00ab\u03ba\u03b1\u03c4\u03b1\u03bb\u03b1\u03b2\u03b1\u03af\u03bd\u03b5\u03b9 Urdu\u00bb \u03ae \u03cc\u03c4\u03b9 \u03ad\u03c7\u03b5\u03b9 \u03c5\u03c8\u03b7\u03bb\u03cc \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc benchmark score. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf script, policy \u03ba\u03b1\u03b9 \u03ba\u03bf\u03b9\u03bd\u03cc.<\/p>\n<p>\u0397 \u03af\u03b4\u03b9\u03b1 \u03b1\u03c1\u03c7\u03ae \u03b9\u03c3\u03c7\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 AI \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03bd\u03ad\u03b1 \u03b1\u03b3\u03bf\u03c1\u03ac: \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/geographic-domain-shift-ai-modelo-se-nees-agores\/\">geographic domain shift<\/a> \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03cc\u03c4\u03b1\u03bd \u03b7 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03af\u03b4\u03b9\u03b1, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b1\u03bb\u03bb\u03ac\u03b6\u03bf\u03c5\u03bd \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1, \u03c3\u03c5\u03c7\u03bd\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2, \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2 \u03c7\u03c1\u03ae\u03c3\u03b7\u03c2.<\/p>\n<h2 id=\"lora-apotelesmata\">\u03a0\u03ce\u03c2 \u03c4\u03bf LoRA \u03ac\u03bb\u03bb\u03b1\u03be\u03b5 \u03c4\u03b7\u03bd \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7<\/h2>\n<p>\u03a4\u03bf LoRA \u03b5\u03b9\u03c3\u03ac\u03b3\u03b5\u03b9 \u03bc\u03b9\u03ba\u03c1\u03bf\u03cd\u03c2 low-rank adapters \u03c3\u03b5 \u03b5\u03c0\u03b9\u03bb\u03b5\u03b3\u03bc\u03ad\u03bd\u03b1 layers \u03ba\u03b1\u03b9 \u03ba\u03c1\u03b1\u03c4\u03ac \u03c0\u03b1\u03b3\u03c9\u03bc\u03ad\u03bd\u03b1 \u03c4\u03b1 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ac weights. \u03a3\u03c4\u03b7 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1, \u03b7 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c3\u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bd\u03b1 \u03bc\u03ac\u03b8\u03b5\u03b9 spelling variants, \u03c4\u03bf\u03c0\u03b9\u03ba\u03ac \u03bc\u03bf\u03c4\u03af\u03b2\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03b7 binary policy \u03c4\u03bf\u03c5 dataset \u03c7\u03c9\u03c1\u03af\u03c2 \u03c0\u03bb\u03ae\u03c1\u03b7 \u03b5\u03bd\u03b7\u03bc\u03ad\u03c1\u03c9\u03c3\u03b7 \u03b4\u03b9\u03c3\u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03c5\u03c1\u03af\u03c9\u03bd \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd.<\/p>\n<p>\u039c\u03b5\u03c4\u03ac \u03c4\u03bf PEFT, \u03c4\u03bf Mistral-7B-v0.3 \u03c6\u03c4\u03ac\u03bd\u03b5\u03b9 F1 0,9387 \u03ba\u03b1\u03b9 accuracy 0,9642, \u03b5\u03bd\u03ce \u03c4\u03bf Meta-Llama-3-8B F1 0,9379 \u03ba\u03b1\u03b9 accuracy 0,9640 \u03c3\u03c4\u03bf\u03bd \u03b1\u03bd\u03b1\u03bb\u03c5\u03c4\u03b9\u03ba\u03cc \u03c0\u03af\u03bd\u03b1\u03ba\u03b1. \u03a4\u03bf Gemma-2B \u03c3\u03b7\u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 F1 0,9089, \u03c4\u03bf multilingual BERT 0,8203 \u03ba\u03b1\u03b9 \u03c4\u03bf DeepSeek-R1-7B 0,7468. \u039f Falcon \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 F1 0,9180 \u03c3\u03c4\u03bf\u03bd \u03b1\u03bd\u03b1\u03bb\u03c5\u03c4\u03b9\u03ba\u03cc \u03c0\u03af\u03bd\u03b1\u03ba\u03b1.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<div class=\"td-chart-head\">\n<p class=\"td-chart-title\">\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b9\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af \u03c0\u03bf\u03c5 \u03bf\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c4\u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7<\/p>\n<p class=\"td-chart-subtitle\">\u039f\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c4\u03c1\u03b9\u03b2\u03ae\u00b7 \u03c4\u03b1 LoRA \u03ba\u03b1\u03b9 prompting scores \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b7 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae.<\/p>\n<\/div>\n<div class=\"td-chart-body\">\n<div class=\"td-metric-grid\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">72.771<\/span><span class=\"td-metric-label\">\u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03ac parallel \u03c3\u03c7\u03cc\u03bb\u03b9\u03b1 \u03c3\u03c4\u03bf PURUTT<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">13.097<\/span><span class=\"td-metric-label\">toxic \u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03c3\u03c4\u03b7 \u03bc\u03b5\u03b9\u03bf\u03c8\u03b7\u03c6\u03b9\u03ba\u03ae \u03ba\u03bb\u03ac\u03c3\u03b7<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">0,9387<\/span><span class=\"td-metric-label\">\u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03bf F1 \u03c4\u03bf\u03c5 Mistral \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf LoRA<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">0,8565<\/span><span class=\"td-metric-label\">\u03bc\u03ad\u03c3\u03bf F1 \u03c4\u03bf\u03c5 GPT-3.5 few-shot prompting<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>\u03a4\u03bf trainable footprint \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03ac\u03b3\u03bc\u03b1\u03c4\u03b9 \u03c0\u03bf\u03bb\u03cd \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 model, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03c9\u03c1\u03b5\u03ac\u03bd. \u0391\u03c0\u03b1\u03b9\u03c4\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c3\u03b7\u03bc\u03b1\u03c3\u03bc\u03ad\u03bd\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, GPU \u03c7\u03c1\u03cc\u03bd\u03bf\u03c2, reproducible split, adapter registry, regression tests \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 \u03cc\u03c4\u03b1\u03bd \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03b7 policy. \u03a3\u03b5 \u03bf\u03c1\u03b3\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u03bc\u03b5 \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03b1\u03b3\u03bf\u03c1\u03ad\u03c2, \u03ad\u03bd\u03b1\u03c2 adapter \u03b1\u03bd\u03ac \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1 \u03ae use case \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc\u03c2 \u03bc\u03cc\u03bd\u03bf \u03b1\u03bd \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03c3\u03b1\u03c6\u03ae\u03c2 \u03b9\u03b4\u03b9\u03bf\u03ba\u03c4\u03b7\u03c3\u03af\u03b1 \u03ba\u03b1\u03b9 lifecycle.<\/p>\n<p>\u0393\u03b9\u03b1 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03b5\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03c3\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf AI workflow \u03c0\u03b1\u03c1\u03ac \u03c3\u03b5 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03bf \u03ba\u03b1\u03bd\u03cc\u03bd\u03b1 \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03b9\u03c3\u03bc\u03bf\u03cd. \u0397 <a href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u03c5\u03c0\u03b7\u03c1\u03b5\u03c3\u03af\u03b1 \u0391\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03af \u0395\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03c9\u03bd &amp; AI \u03c4\u03b7\u03c2 TWO DOTS<\/a> \u03be\u03b5\u03ba\u03b9\u03bd\u03ac \u03b1\u03c0\u03cc \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03ac\u03c6\u03b7\u03c3\u03b7 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1\u03c2, \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, approvals \u03ba\u03b1\u03b9 monitoring \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03ad\u03bd\u03b1 model score \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03b7 \u03c4\u03b7 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1.<\/p>\n<h2 id=\"prompt-tuning-few-shot\">Prompt tuning \u03ba\u03b1\u03b9 few-shot prompting \u03c3\u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7<\/h2>\n<p>\u03a4\u03bf prompt-tuning \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af OpenPrompt \u03ba\u03b1\u03b9 multilingual BERT. \u03a4\u03b1 manual templates \u03b5\u03af\u03bd\u03b1\u03b9 prefix \u03ae cloze \u03ba\u03b1\u03b9 \u03b6\u03b7\u03c4\u03bf\u03cd\u03bd \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 label \u03bc\u03ad\u03c3\u03c9 \u03b5\u03bd\u03cc\u03c2 masked token \u03ba\u03b1\u03b9 verbalizer. \u0397 mixed \u03b5\u03ba\u03b4\u03bf\u03c7\u03ae \u03c3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 \u03c7\u03b5\u03b9\u03c1\u03bf\u03c0\u03bf\u03af\u03b7\u03c4\u03b7 \u03b4\u03bf\u03bc\u03ae \u03bc\u03b5 learnable soft tokens. \u039f\u03b9 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03b3\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b5 K \u03af\u03c3\u03bf \u03bc\u03b5 32, 64 \u03ba\u03b1\u03b9 128 \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 training \u03ba\u03b1\u03b9 \u03b9\u03c3\u03ac\u03c1\u03b9\u03b8\u03bc\u03b1 \u03b3\u03b9\u03b1 development, \u03c3\u03b5 \u03b4\u03cd\u03bf \u03bc\u03b7 \u03b5\u03c0\u03b9\u03ba\u03b1\u03bb\u03c5\u03c0\u03c4\u03cc\u03bc\u03b5\u03bd\u03bf\u03c5\u03c2 \u03b3\u03cd\u03c1\u03bf\u03c5\u03c2 \u03b1\u03bd\u03ac configuration.<\/p>\n<p>\u03a3\u03c4\u03b1 128 \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1, \u03c4\u03bf mixed prompt \u03ad\u03c7\u03b5\u03b9 \u03c4\u03bf \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf F1, 0,6852. \u03a4\u03bf prefix \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b5\u03af \u03bc\u03b5 0,6813 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf accuracy 0,8048, \u03b5\u03bd\u03ce \u03c4\u03bf cloze \u03c6\u03c4\u03ac\u03bd\u03b5\u03b9 F1 0,6167. \u0397 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf backbone \u03b7 \u03b4\u03b9\u03b1\u03c4\u03cd\u03c0\u03c9\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03b7 learned prompt representation \u03bc\u03b5\u03c4\u03b1\u03ba\u03b9\u03bd\u03bf\u03cd\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac precision, recall \u03ba\u03b1\u03b9 accuracy.<\/p>\n<p>\u03a3\u03c4\u03bf prompt-engineering \u03c3\u03ba\u03ad\u03bb\u03bf\u03c2, \u03c4\u03bf GPT-3.5 \u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03b5\u03af\u03c4\u03b5 \u03bc\u03cc\u03bd\u03bf instruction \u03b5\u03af\u03c4\u03b5 \u03c0\u03ad\u03bd\u03c4\u03b5 toxic \u03ba\u03b1\u03b9 \u03c0\u03ad\u03bd\u03c4\u03b5 non-toxic \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1. \u03a4\u03bf few-shot configuration \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03bc\u03ad\u03c3\u03bf F1 0,8565, accuracy 0,9100, precision 0,8278 \u03ba\u03b1\u03b9 recall 0,8989. \u03a4\u03bf zero-shot \u03ad\u03c7\u03b5\u03b9 \u03bc\u03ad\u03c3\u03bf F1 0,6698. \u0388\u03bd\u03b1 \u03bc\u03b5\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03bf batch \u03c6\u03c4\u03ac\u03bd\u03b5\u03b9 F1 0,9494, \u03b1\u03bb\u03bb\u03ac \u03c4\u03bf peak \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03ad\u03c3\u03b7 \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u03bf\u03cd\u03c4\u03b5 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2.<\/p>\n<div class=\"td-comparison td-comparison-cards td-comparison-cards--horizontal\">\n<div class=\"td-comparison-grid td-comparison-grid--three\">\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">Direct inference<\/p>\n<p>\u0395\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03b7 \u03c0\u03c1\u03bf\u03b5\u03c4\u03bf\u03b9\u03bc\u03b1\u03c3\u03af\u03b1, \u03b1\u03bb\u03bb\u03ac \u03c3\u03c4\u03bf baseline \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc macro F1 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7 \u03bc\u03b5\u03c4\u03b1\u03c4\u03cc\u03c0\u03b9\u03c3\u03b7 \u03c0\u03c1\u03bf\u03c2 toxic \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03b9\u03c2 \u03b3\u03b9\u03b1 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u0393\u03c1\u03ae\u03b3\u03bf\u03c1\u03bf baseline<\/span><span class=\"td-badge\">\u03a5\u03c8\u03b7\u03bb\u03ae \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<p class=\"td-comparison-title\">Few-shot prompting<\/p>\n<p>\u0392\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03b5\u03b9 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b1 \u03c4\u03b7 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03c7\u03c9\u03c1\u03af\u03c2 training, \u03b1\u03bb\u03bb\u03ac \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03b9\u03b3\u03bc\u03ac\u03c4\u03c9\u03bd, \u03c4\u03bf prompt, \u03c4\u03bf batch \u03ba\u03b1\u03b9 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac \u03b1\u03af\u03c4\u03b7\u03bc\u03b1.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u03a7\u03c9\u03c1\u03af\u03c2 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae weights<\/span><span class=\"td-badge\">Prompt sensitivity<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">LoRA adapters<\/p>\n<p>\u0394\u03af\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03b7 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03ae \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7, \u03bc\u03b5 \u03b1\u03bd\u03c4\u03ac\u03bb\u03bb\u03b1\u03b3\u03bc\u03b1 dataset, training, versioning \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03b5\u03c7\u03ae regression \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u03a4\u03bf\u03c0\u03b9\u03ba\u03ae \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae<\/span><span class=\"td-badge\">Lifecycle \u03b5\u03c5\u03b8\u03cd\u03bd\u03b7<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>\u03a4\u03bf prompting \u03b5\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2 \u03b4\u03b5\u03bd \u00ab\u03b1\u03c0\u03ad\u03c4\u03c5\u03c7\u03b5\u00bb. \u03a0\u03b1\u03c1\u03b5\u03af\u03c7\u03b5 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc proof of concept \u03c7\u03c9\u03c1\u03af\u03c2 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae weights \u03ba\u03b1\u03b9 \u03be\u03b5\u03c0\u03ad\u03c1\u03b1\u03c3\u03b5 \u03c3\u03b1\u03c6\u03ce\u03c2 \u03c4\u03bf zero-shot \u03c3\u03c4\u03bf\u03bd \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf. \u03a4\u03bf LoRA \u03ba\u03ad\u03c1\u03b4\u03b9\u03c3\u03b5 \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf \u03b6\u03b7\u03c4\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf \u03ae\u03c4\u03b1\u03bd \u03b7 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03b7 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03ae \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03c3\u03c4\u03bf \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03bf corpus. \u0397 \u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03b7 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc data volume, \u03c3\u03c5\u03c7\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 policy changes, latency, \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 inference \u03ba\u03b1\u03b9 \u03b1\u03bd\u03ac\u03b3\u03ba\u03b7 \u03b5\u03c3\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03bf\u03cd \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5.<\/p>\n<h2 id=\"ti-apodeiknyei-sygkrisi\">\u03a4\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03c4\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7<\/h2>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c0\u03b1\u03c1\u03ad\u03c7\u03b5\u03b9 \u03c0\u03b5\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03ad\u03bd\u03b4\u03b5\u03b9\u03be\u03b7 \u03cc\u03c4\u03b9 task-specific adaptation \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03c3\u03b5\u03b9 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03ad\u03bd\u03b1 low-resource moderation task. \u03a4\u03bf \u03b5\u03cd\u03c1\u03b7\u03bc\u03b1 \u03b5\u03bd\u03b9\u03c3\u03c7\u03cd\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7 \u03c3\u03c5\u03bd\u03b1\u03c6\u03ae \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7 \u03c4\u03c9\u03bd Zubair \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03b5\u03c1\u03b3\u03b1\u03c4\u03ce\u03bd, \u03b7 \u03bf\u03c0\u03bf\u03af\u03b1 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 zero-shot \u03ba\u03b1\u03b9 PEFT \u03c3\u03c4\u03bf PURUTT \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 F1 \u03c0\u03ac\u03bd\u03c9 \u03b1\u03c0\u03cc 0,93 \u03b3\u03b9\u03b1 \u03c4\u03b1 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 adapted models.<\/p>\n<p>\u0394\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03bc\u03c9\u03c2 \u03cc\u03c4\u03b9 \u03c4\u03bf LoRA \u03c5\u03c0\u03b5\u03c1\u03ad\u03c7\u03b5\u03b9 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 low-resource \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1, \u03bf\u03cd\u03c4\u03b5 \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1 F1 \u03ba\u03bf\u03bd\u03c4\u03ac \u03c3\u03c4\u03bf 0,94 \u03b1\u03c1\u03ba\u03b5\u03af \u03b3\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae. \u03a4\u03bf corpus \u03b1\u03c6\u03bf\u03c1\u03ac Roman Urdu, \u03b7 \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 binary, \u03c4\u03b1 labels \u03b4\u03b5\u03bd \u03b4\u03b9\u03b1\u03c7\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03b8\u03c1\u03b7\u03c3\u03ba\u03b5\u03c5\u03c4\u03b9\u03ba\u03cc, \u03ad\u03bc\u03c6\u03c5\u03bb\u03bf \u03ae \u03c6\u03c5\u03bb\u03b5\u03c4\u03b9\u03ba\u03cc hate speech \u03ba\u03b1\u03b9 \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b4\u03b5\u03bd \u03b4\u03af\u03bd\u03b5\u03b9 slice-level fairness \u03b1\u03bd\u03ac \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ae, spelling style \u03ae \u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03b9\u03ba\u03ae \u03bf\u03bc\u03ac\u03b4\u03b1.<\/p>\n<p>\u0394\u03b5\u03bd \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b1 documents, \u03b5\u03bd\u03c4\u03bf\u03c0\u03b9\u03c3\u03bc\u03cc\u03c2 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03c9\u03bd harmful spans, \u03ba\u03c9\u03b4\u03b9\u03ba\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03b7 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1, adversarial evasion \u03ae \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03cc drift. \u0397 \u03b5\u03c0\u03b9\u03c4\u03c5\u03c7\u03af\u03b1 \u03c3\u03c4\u03bf held-out set \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b5\u03b9\u03c9\u03b8\u03b5\u03af \u03cc\u03c4\u03b1\u03bd \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03b7 \u03c0\u03bb\u03b1\u03c4\u03c6\u03cc\u03c1\u03bc\u03b1, \u03b7 \u03ba\u03bf\u03b9\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ae \u03b7 \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae. \u0393\u03b9\u03b1 \u03b1\u03c5\u03c4\u03cc \u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 temporal holdout \u03ba\u03b1\u03b9 slices \u03c0\u03bf\u03c5 \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf\u03cd\u03bd \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03b3\u03bf\u03c1\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03bc\u03bf\u03c1\u03c6\u03ad\u03c2 \u03c7\u03c1\u03ae\u03c3\u03b7\u03c2.<\/p>\n<p>\u0397 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b8\u03c5\u03bc\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1 \u03c4\u03bf\u03c5 <a href=\"https:\/\/twodots.gr\/ai-alignment-sosti-apantisi-sosti-logiki\/\">AI alignment \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7<\/a>: \u03b7 \u03c3\u03c5\u03bc\u03c6\u03c9\u03bd\u03af\u03b1 \u03c3\u03b5 \u03ad\u03bd\u03b1 label \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03b1\u03bd \u03b4\u03b5\u03bd \u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bc\u03b5 \u03c0\u03bf\u03b9\u03bf\u03bd \u03ba\u03b1\u03bd\u03cc\u03bd\u03b1 \u03b5\u03c6\u03ac\u03c1\u03bc\u03bf\u03c3\u03b5 \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1, \u03c3\u03b5 \u03c0\u03bf\u03b9\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b2\u03b1\u03c3\u03af\u03c3\u03c4\u03b7\u03ba\u03b5 \u03ba\u03b1\u03b9 \u03c0\u03ce\u03c2 \u03b8\u03b1 \u03b1\u03bc\u03c6\u03b9\u03c3\u03b2\u03b7\u03c4\u03b7\u03b8\u03b5\u03af \u03ad\u03bd\u03b1 \u03bb\u03ac\u03b8\u03bf\u03c2 \u03c5\u03c8\u03b7\u03bb\u03bf\u03cd \u03b1\u03bd\u03c4\u03af\u03ba\u03c4\u03c5\u03c0\u03bf\u03c5.<\/p>\n<h2 id=\"asynepeies-pinakon\">\u039f\u03b9 \u03b1\u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03b9\u03b5\u03c2 \u03c4\u03c9\u03bd \u03c0\u03b9\u03bd\u03ac\u03ba\u03c9\u03bd \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03b3\u03bd\u03bf\u03b7\u03b8\u03bf\u03cd\u03bd<\/h2>\n<p>\u0397 \u03b4\u03b9\u03b1\u03c4\u03c1\u03b9\u03b2\u03ae \u03b4\u03b7\u03bb\u03ce\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 baseline \u03ba\u03b1\u03b9 post-PEFT \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd \u03c4\u03bf \u03af\u03b4\u03b9\u03bf held-out test split. \u03a9\u03c3\u03c4\u03cc\u03c3\u03bf, \u03bf baseline \u03c0\u03af\u03bd\u03b1\u03ba\u03b1\u03c2 label counts \u03b1\u03b8\u03c1\u03bf\u03af\u03b6\u03b5\u03b9 4.964 \u03c3\u03c7\u03cc\u03bb\u03b9\u03b1, \u03b5\u03bd\u03ce \u03bf post-PEFT \u03c0\u03af\u03bd\u03b1\u03ba\u03b1\u03c2 \u03b1\u03b8\u03c1\u03bf\u03af\u03b6\u03b5\u03b9 14.555, \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 \u03c3\u03c4\u03bf 20% \u03c4\u03bf\u03c5 \u03c0\u03bb\u03ae\u03c1\u03bf\u03c5\u03c2 corpus. \u03a4\u03b1 raw counts \u03b5\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2 \u03b4\u03b5\u03bd \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03c3\u03c5\u03b3\u03ba\u03c1\u03b9\u03b8\u03bf\u03cd\u03bd \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ac \u03c9\u03c2 \u03b4\u03cd\u03bf \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf.<\/p>\n<p>\u03a5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03b5\u03c3\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ae \u03b1\u03c3\u03c5\u03bc\u03b2\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c3\u03c4\u03bf\u03bd baseline confusion matrix. \u0393\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03b7 \u03b3\u03c1\u03b1\u03bc\u03bc\u03ae \u03c4\u03bf\u03c5 Mistral \u03b1\u03b8\u03c1\u03bf\u03af\u03b6\u03b5\u03b9 TP, TN, FP \u03ba\u03b1\u03b9 FN \u03c3\u03b5 7.915 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2, \u03cc\u03c7\u03b9 \u03c3\u03c4\u03b9\u03c2 4.964 \u03c4\u03bf\u03c5 \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf\u03c5 \u03c0\u03af\u03bd\u03b1\u03ba\u03b1, \u03b5\u03bd\u03ce \u03c4\u03b1 predicted counts \u03b4\u03b5\u03bd \u03c0\u03c1\u03bf\u03ba\u03cd\u03c0\u03c4\u03bf\u03c5\u03bd \u03b1\u03c0\u03cc \u03c4\u03b1 \u03af\u03b4\u03b9\u03b1 \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1. \u03a3\u03b5 \u03ac\u03bb\u03bb\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03c4\u03bf BERT \u03c7\u03c9\u03c1\u03af\u03c2 fine-tuning \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 F1 0,15 \u03ba\u03b1\u03b9 \u03b1\u03c1\u03b3\u03cc\u03c4\u03b5\u03c1\u03b1 \u03bc\u03b5 0,30. \u039f \u03b5\u03bd\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03bf\u03c2 \u03c0\u03af\u03bd\u03b1\u03ba\u03b1\u03c2 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2 accuracy \u03ba\u03b1\u03b9 F1 \u03c3\u03b5 \u03c3\u03c7\u03ad\u03c3\u03b7 \u03bc\u03b5 \u03c4\u03bf\u03c5\u03c2 \u03b1\u03bd\u03b1\u03bb\u03c5\u03c4\u03b9\u03ba\u03bf\u03cd\u03c2.<\/p>\n<aside class=\"td-article-note\"><strong>\u03a4\u03b9 \u03ba\u03c1\u03b1\u03c4\u03ac\u03bc\u03b5 \u03bc\u03b5 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1:<\/strong> \u03bf\u03b9 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03bf\u03b9 aggregate \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2 \u03b4\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03c3\u03b1\u03c6\u03ae \u03ba\u03b1\u03c4\u03b5\u03cd\u03b8\u03c5\u03bd\u03c3\u03b7 \u03c5\u03c0\u03ad\u03c1 \u03c4\u03bf\u03c5 LoRA \u03ba\u03b1\u03b9 \u03c5\u03c0\u03ad\u03c1 \u03c4\u03bf\u03c5 few-shot \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 zero-shot. \u0394\u03b5\u03bd \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bc\u03b5 \u03cc\u03bc\u03c9\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03b1\u03c3\u03cd\u03bc\u03b2\u03b1\u03c4\u03bf\u03c5\u03c2 raw confusion counts \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03af\u03c3\u03bf\u03c5\u03bc\u03b5 \u03bd\u03ad\u03b1 \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03ac, \u03bf\u03cd\u03c4\u03b5 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03b6\u03bf\u03c5\u03bc\u03b5 \u03c4\u03bf ranking \u03c9\u03c2 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b1 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03c7\u03b8\u03ad\u03bd benchmark.<\/aside>\n<p>\u0397 \u03c3\u03c9\u03c3\u03c4\u03ae \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03ae \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 reproducible evaluation package: \u03b1\u03bc\u03b5\u03c4\u03ac\u03b2\u03bb\u03b7\u03c4\u03b1 sample IDs, hash \u03c4\u03bf\u03c5 dataset, split manifest, model \u03ba\u03b1\u03b9 adapter version, prompt version, code commit, threshold, class mapping \u03ba\u03b1\u03b9 \u03c0\u03bb\u03ae\u03c1\u03b7\u03c2 \u03ad\u03be\u03bf\u03b4\u03bf\u03c2 \u03b1\u03bd\u03ac \u03b4\u03b5\u03af\u03b3\u03bc\u03b1. \u03a7\u03c9\u03c1\u03af\u03c2 \u03b1\u03c5\u03c4\u03ac, \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03c3\u03c9\u03c3\u03c4\u03bf\u03af \u03c3\u03c5\u03bd\u03bf\u03c0\u03c4\u03b9\u03ba\u03bf\u03af \u03b4\u03b5\u03af\u03ba\u03c4\u03b5\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03cd\u03c3\u03ba\u03bf\u03bb\u03bf \u03bd\u03b1 \u03b5\u03bb\u03b5\u03b3\u03c7\u03b8\u03bf\u03cd\u03bd \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5.<\/p>\n<h2 id=\"moderation-paragogi\">\u0391\u03c0\u03cc \u03c4\u03bf classifier \u03c3\u03b5 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ae \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1 moderation<\/h2>\n<p>\u0388\u03bd\u03b1 moderation \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd \u03b4\u03b5\u03bd \u03c4\u03b5\u03bb\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c3\u03c4\u03b7\u03bd \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 toxic\/non-toxic. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 policy taxonomy \u03c0\u03bf\u03c5 \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 hate speech, \u03c0\u03c1\u03bf\u03c3\u03b2\u03bf\u03bb\u03ae, \u03b1\u03c0\u03b5\u03b9\u03bb\u03ae, \u03c0\u03b1\u03c1\u03b5\u03bd\u03cc\u03c7\u03bb\u03b7\u03c3\u03b7, spam \u03ba\u03b1\u03b9 \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03cc product feedback. \u039a\u03ac\u03b8\u03b5 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae \u03b2\u03bb\u03ac\u03b2\u03b7, \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03c4\u03b9\u03ba\u03cc \u03cc\u03c1\u03b9\u03bf \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c4\u03c1\u03b5\u03c0\u03c4\u03ae \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1.<\/p>\n<p>\u03a4\u03bf threshold \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03b5\u03c5\u03b8\u03b5\u03af\u03b1\u03c2 \u03c3\u03b5 \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03b9\u03c3\u03bc\u03cc. \u039c\u03b9\u03b1 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c3\u03c4\u03b5\u03c1\u03b7 \u03c1\u03bf\u03ae \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03c1\u03cd\u03b2\u03b5\u03b9 \u03c0\u03c1\u03bf\u03c3\u03c9\u03c1\u03b9\u03bd\u03ac \u03bc\u03cc\u03bd\u03bf \u03c0\u03b5\u03c1\u03b9\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf \u03c0\u03bf\u03bb\u03cd \u03c5\u03c8\u03b7\u03bb\u03bf\u03cd \u03ba\u03b9\u03bd\u03b4\u03cd\u03bd\u03bf\u03c5, \u03bd\u03b1 \u03c3\u03c4\u03ad\u03bb\u03bd\u03b5\u03b9 \u03c4\u03b9\u03c2 \u03b5\u03bd\u03b4\u03b9\u03ac\u03bc\u03b5\u03c3\u03b5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2 \u03c3\u03b5 reviewer \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03c6\u03ae\u03bd\u03b5\u03b9 \u03c4\u03bf \u03c7\u03b1\u03bc\u03b7\u03bb\u03bf\u03cd \u03ba\u03b9\u03bd\u03b4\u03cd\u03bd\u03bf\u03c5 \u03c0\u03b5\u03c1\u03b9\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf \u03bf\u03c1\u03b1\u03c4\u03cc \u03bc\u03b5 monitoring. \u0397 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ae\u03c2 \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd, \u03c4\u03b7 \u03b4\u03b9\u03ba\u03b1\u03b9\u03bf\u03b4\u03bf\u03c3\u03af\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ad\u03b3\u03ba\u03b1\u03b9\u03c1\u03b7\u03c2 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7\u03c2 \u03c0\u03b1\u03c1\u03ad\u03bc\u03b2\u03b1\u03c3\u03b7\u03c2.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-kicker\">Production gate \u03b3\u03b9\u03b1 AI moderation<\/p>\n<p class=\"td-decision-title\">\u039a\u03b1\u03bd\u03ad\u03bd\u03b1 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc F1 \u03b4\u03b5\u03bd \u03b4\u03b9\u03ba\u03b1\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf\u03c5 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b7 \u03ba\u03cd\u03c1\u03c9\u03c3\u03b7<\/p>\n<p>\u03a0\u03c1\u03bf\u03c7\u03c9\u03c1\u03ae\u03c3\u03c4\u03b5 \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b1\u03bd \u03ad\u03c7\u03bf\u03c5\u03bd \u03bf\u03c1\u03b9\u03c3\u03c4\u03b5\u03af policy-specific labels, precision \u03ba\u03b1\u03b9 recall \u03b1\u03bd\u03ac \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf slice, calibrated thresholds, reviewer SLA, \u03ba\u03b1\u03c4\u03b1\u03b3\u03b5\u03b3\u03c1\u03b1\u03bc\u03bc\u03ad\u03bd\u03bf audit trail, appeals \u03ba\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 fallback. \u039a\u03ac\u03b8\u03b5 \u03bd\u03ad\u03b1 \u03b1\u03b3\u03bf\u03c1\u03ac, adapter \u03ae \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b5\u03c1\u03bd\u03ac \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 regression suite \u03c0\u03c1\u03b9\u03bd \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03c3\u03b5\u03b9 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b7.<\/p>\n<\/div>\n<p>\u0397 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1 \u03b1\u03c5\u03c4\u03ae \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 abstention. \u038c\u03c4\u03b1\u03bd \u03c4\u03bf \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bc\u03c6\u03af\u03c3\u03b7\u03bc\u03bf, \u03c0\u03b5\u03c1\u03b9\u03ad\u03c7\u03b5\u03b9 \u03ba\u03c9\u03b4\u03b9\u03ba\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03b7 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1 \u03ae \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03ad\u03be\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03b3\u03bd\u03c9\u03c3\u03c4\u03cc distribution, \u03c4\u03bf \u03c3\u03c9\u03c3\u03c4\u03cc \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u00ab\u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c6\u03b1\u03c3\u03af\u03b6\u03c9 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b1\u00bb. \u0397 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/coresec-ai-abstention-root-cause-analysis\/\">AI abstention \u03ba\u03b1\u03b9 \u03c4\u03bf root-cause analysis<\/a> \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03b7 \u03b1\u03c0\u03cc\u03c1\u03c1\u03b9\u03c8\u03b7 \u03bc\u03b9\u03b1\u03c2 \u03b1\u03b2\u03ad\u03b2\u03b1\u03b9\u03b7\u03c2 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b1\u03bd \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c6\u03b5\u03c4\u03b1\u03b9 \u03bf \u03bb\u03cc\u03b3\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03b7 \u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03b7 \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ae.<\/p>\n<p>\u0393\u03b9\u03b1 e-commerce, \u03c4\u03b1 KPIs \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b4\u03ad\u03bf\u03c5\u03bd \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1: precision \u03c3\u03c4\u03b7\u03bd \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b7 \u03b1\u03c0\u03cc\u03ba\u03c1\u03c5\u03c8\u03b7, recall \u03c3\u03c4\u03b9\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03bf\u03b4\u03b7\u03b3\u03bf\u03cd\u03bd \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b2\u03bb\u03ac\u03b2\u03b7, review time, overturn rate \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc appeal, \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03cc escalation \u03ba\u03b1\u03b9 \u03c0\u03b1\u03c1\u03ac\u03c0\u03bf\u03bd\u03b1 \u03b1\u03bd\u03ac \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1. \u038c\u03c0\u03c9\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b9\u03c2 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ad\u03c2 <a href=\"https:\/\/twodots.gr\/techniti-noimosyni-automation-ecommerce-nvh-testing\/\">AI \u03c3\u03c4\u03bf e-commerce<\/a>, \u03b7 \u03b1\u03be\u03af\u03b1 \u03c0\u03c1\u03bf\u03ba\u03cd\u03c0\u03c4\u03b5\u03b9 \u03b1\u03c0\u03cc \u03ba\u03b1\u03b8\u03b1\u03c1\u03cc pain point \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf workflow, \u03cc\u03c7\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03af\u03b1 \u03b5\u03bd\u03cc\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5.<\/p>\n<h2 id=\"dataset-governance\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 \u03b4\u03b9\u03b1\u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 dataset \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2<\/h2>\n<p>\u0397 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf\u03c5 classifier \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03bf\u03b9\u03bf\u03c2 \u03cc\u03c1\u03b9\u03c3\u03b5 \u03c4\u03bf toxic label, \u03c0\u03bf\u03b9\u03b1 \u03c3\u03c7\u03cc\u03bb\u03b9\u03b1 \u03b5\u03c0\u03b9\u03bb\u03ad\u03c7\u03b8\u03b7\u03ba\u03b1\u03bd \u03ba\u03b1\u03b9 \u03c0\u03bf\u03b9\u03b1 \u03c3\u03c5\u03bc\u03c6\u03c1\u03b1\u03b6\u03cc\u03bc\u03b5\u03bd\u03b1 \u03ad\u03bc\u03b5\u03b9\u03bd\u03b1\u03bd \u03b5\u03ba\u03c4\u03cc\u03c2. \u03a3\u03b5 \u03bc\u03b9\u03b1 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1 \u03bc\u03b5 \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03ac\u03c4\u03c5\u03c0\u03b5\u03c2 \u03b3\u03c1\u03b1\u03c6\u03ad\u03c2, \u03b7 \u03b4\u03b5\u03b9\u03b3\u03bc\u03b1\u03c4\u03bf\u03bb\u03b7\u03c8\u03af\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 \u03bf\u03c1\u03b8\u03bf\u03b3\u03c1\u03b1\u03c6\u03b9\u03ba\u03ad\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2, \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ad\u03c2, \u03b7\u03bb\u03b9\u03ba\u03b9\u03b1\u03ba\u03ad\u03c2 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2, code-switching \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd\u03c4\u03b1 \u03c3\u03c5\u03bd\u03bf\u03bc\u03b9\u03bb\u03af\u03b1\u03c2. \u0394\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac, \u03ad\u03bd\u03b1 \u03c5\u03c8\u03b7\u03bb\u03cc \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc F1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03c1\u03cd\u03b2\u03b5\u03b9 \u03ac\u03bd\u03b9\u03c3\u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1.<\/p>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b4\u03b5\u03bd \u03b1\u03bd\u03b1\u03bb\u03cd\u03b5\u03b9 annotator agreement \u03ae slice-level fairness. \u039c\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03ae \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 datasheet \u03bc\u03b5 \u03c0\u03c1\u03bf\u03ad\u03bb\u03b5\u03c5\u03c3\u03b7, \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ae \u03c0\u03b5\u03c1\u03af\u03bf\u03b4\u03bf, \u03b4\u03b9\u03ba\u03b1\u03b9\u03ce\u03bc\u03b1\u03c4\u03b1 \u03c7\u03c1\u03ae\u03c3\u03b7\u03c2, \u03ba\u03b1\u03bd\u03cc\u03bd\u03b5\u03c2 \u03b1\u03bd\u03c9\u03bd\u03c5\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2, \u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 labels, reviewer expertise \u03ba\u03b1\u03b9 \u03b3\u03bd\u03c9\u03c3\u03c4\u03ac \u03ba\u03b5\u03bd\u03ac. \u039f\u03b9 \u03b4\u03b9\u03b1\u03c6\u03c9\u03bd\u03af\u03b5\u03c2 \u03c4\u03c9\u03bd annotators \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b8\u03cc\u03c1\u03c5\u03b2\u03bf\u03c2 \u03c0\u03bf\u03c5 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03bd\u03b1 \u03b5\u03be\u03b1\u03c6\u03b1\u03bd\u03b9\u03c3\u03c4\u03b5\u03af\u00b7 \u03c3\u03c5\u03c7\u03bd\u03ac \u03b1\u03c0\u03bf\u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03bf\u03c5\u03bd \u03b1\u03c3\u03b1\u03c6\u03ae policy \u03ae \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03c3\u03bc\u03b9\u03ba\u03cc context.<\/p>\n<p>\u039f\u03b9 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2 moderators \u03ba\u03b1\u03b9 \u03c4\u03b1 \u03b5\u03c0\u03b9\u03c4\u03c5\u03c7\u03b7\u03bc\u03ad\u03bd\u03b1 appeals \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03c4\u03c1\u03bf\u03c6\u03bf\u03b4\u03bf\u03c4\u03bf\u03cd\u03bd \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf \u03ba\u03cd\u03ba\u03bb\u03bf \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7\u03c2, \u03b1\u03bb\u03bb\u03ac \u03cc\u03c7\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b1 \u03c3\u03b5 training examples. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 deduplication, sampling review, label adjudication \u03ba\u03b1\u03b9 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 \u03b3\u03b9\u03b1 feedback loops \u03c0\u03bf\u03c5 \u03b5\u03bd\u03b9\u03c3\u03c7\u03cd\u03bf\u03c5\u03bd \u03c0\u03b1\u03bb\u03b9\u03ad\u03c2 \u03c0\u03c1\u03bf\u03ba\u03b1\u03c4\u03b1\u03bb\u03ae\u03c8\u03b5\u03b9\u03c2.<\/p>\n<p>\u03a4\u03bf NIST AI RMF \u03c0\u03c1\u03bf\u03c3\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03b4\u03bf\u03bc\u03ae: govern \u03b3\u03b9\u03b1 \u03c1\u03cc\u03bb\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03bb\u03bf\u03b3\u03bf\u03b4\u03bf\u03c3\u03af\u03b1, map \u03b3\u03b9\u03b1 context \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b7\u03c1\u03b5\u03b1\u03b6\u03cc\u03bc\u03b5\u03bd\u03bf\u03c5\u03c2 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03bf\u03c5\u03c2, measure \u03b3\u03b9\u03b1 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03bf\u03cd\u03c2 \u03ba\u03b1\u03b9 \u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03b9\u03ba\u03bf\u03cd\u03c2 \u03b4\u03b5\u03af\u03ba\u03c4\u03b5\u03c2 \u03ba\u03b1\u03b9 manage \u03b3\u03b9\u03b1 thresholds, incidents \u03ba\u03b1\u03b9 \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03c3\u03b5\u03b9\u03c2. \u03a4\u03bf framework \u03b4\u03b5\u03bd \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03b9 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, \u03b1\u03bb\u03bb\u03ac \u03b5\u03bc\u03c0\u03bf\u03b4\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf benchmark \u03bd\u03b1 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b5\u03af \u03c9\u03c2 \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 \u03c3\u03c7\u03ad\u03b4\u03b9\u03bf \u03b4\u03b9\u03b1\u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03b7\u03c2.<\/p>\n<h2 id=\"pilot-epta-vimata\">\u0388\u03bd\u03b1 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf pilot \u03c3\u03b5 \u03b5\u03c0\u03c4\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1<\/h2>\n<p>\u03a4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf pilot \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c4\u03b5\u03bd\u03cc: \u03bc\u03af\u03b1 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1, \u03bc\u03af\u03b1 \u03c0\u03b7\u03b3\u03ae \u03c0\u03b5\u03c1\u03b9\u03b5\u03c7\u03bf\u03bc\u03ad\u03bd\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03bc\u03af\u03b1 \u03c3\u03b1\u03c6\u03ae\u03c2 policy. \u03a3\u03c4\u03cc\u03c7\u03bf\u03c2 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03b5\u03af\u03be\u03b5\u03b9 \u03cc\u03c4\u03b9 \u00ab\u03b7 AI \u03ba\u03ac\u03bd\u03b5\u03b9 moderation\u00bb, \u03b1\u03bb\u03bb\u03ac \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9 \u03c0\u03bf\u03cd \u03b2\u03bf\u03b7\u03b8\u03ac, \u03c0\u03bf\u03cd \u03c3\u03c6\u03ac\u03bb\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03c0\u03cc\u03c3\u03b7 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03bc\u03b5\u03c4\u03b1\u03ba\u03b9\u03bd\u03b5\u03af \u03ae \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u0395\u03c0\u03c4\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf dataset \u03ad\u03c9\u03c2 \u03c4\u03bf production gate<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 1<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03c4\u03b7 \u03b2\u03bb\u03ac\u03b2\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03b1 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ae labels<\/strong>\n<p>\u0394\u03b9\u03b1\u03c7\u03c9\u03c1\u03af\u03c3\u03c4\u03b5 hate speech, \u03b1\u03c0\u03b5\u03b9\u03bb\u03ae, \u03c0\u03c1\u03bf\u03c3\u03b2\u03bf\u03bb\u03ae, spam \u03ba\u03b1\u03b9 \u03bd\u03cc\u03bc\u03b9\u03bc\u03b7 \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03ae \u03ba\u03c1\u03b9\u03c4\u03b9\u03ba\u03ae, \u03bc\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03ba\u03b1\u03bd\u03cc\u03bd\u03b5\u03c2 \u03b3\u03b9\u03b1 \u03b1\u03bc\u03c6\u03af\u03b2\u03bf\u03bb\u03b5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 2<\/span><strong>\u03a7\u03c4\u03af\u03c3\u03c4\u03b5 \u03b1\u03bd\u03c4\u03b9\u03c0\u03c1\u03bf\u03c3\u03c9\u03c0\u03b5\u03c5\u03c4\u03b9\u03ba\u03cc, \u03bd\u03cc\u03bc\u03b9\u03bc\u03bf \u03b4\u03b5\u03af\u03b3\u03bc\u03b1<\/strong>\n<p>\u039a\u03b1\u03bb\u03cd\u03c8\u03c4\u03b5 spelling variants, code-switching, \u03b1\u03b3\u03bf\u03c1\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ad\u03c2 \u03c0\u03b5\u03c1\u03b9\u03cc\u03b4\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 \u03c0\u03c1\u03bf\u03ad\u03bb\u03b5\u03c5\u03c3\u03b7, \u03c3\u03c5\u03bd\u03b1\u03af\u03bd\u03b5\u03c3\u03b7 \u03ae \u03ac\u03bb\u03bb\u03bf \u03bd\u03cc\u03bc\u03b9\u03bc\u03bf \u03ad\u03c1\u03b5\u03b9\u03c3\u03bc\u03b1 \u03c7\u03c1\u03ae\u03c3\u03b7\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 3<\/span><strong>\u039a\u03bb\u03b5\u03b9\u03b4\u03ce\u03c3\u03c4\u03b5 holdout \u03ba\u03b1\u03b9 slices<\/strong>\n<p>\u0394\u03b9\u03b1\u03c4\u03b7\u03c1\u03ae\u03c3\u03c4\u03b5 sample IDs \u03ba\u03b1\u03b9 dataset hash \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac \u03c4\u03b9\u03c2 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03b9\u03c2 \u03bf\u03c0\u03bf\u03af\u03b5\u03c2 \u03ad\u03bd\u03b1 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc F1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03c1\u03cd\u03c8\u03b5\u03b9 \u03b1\u03c0\u03bf\u03c4\u03c5\u03c7\u03af\u03b1.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 4<\/span><strong>\u03a3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 prompt \u03ba\u03b1\u03b9 adapter \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf<\/strong>\n<p>\u03a4\u03c1\u03ad\u03be\u03c4\u03b5 direct, few-shot \u03ba\u03b1\u03b9 LoRA \u03bc\u03b5 \u03ba\u03bf\u03b9\u03bd\u03ac labels, test set, thresholds \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ce\u03c3\u03c4\u03b5 \u03b7 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03bd\u03b1 \u03bc\u03b7\u03bd \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03b1\u03c3\u03cd\u03bc\u03b2\u03b1\u03c4\u03bf\u03c5\u03c2 \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 5<\/span><strong>\u0392\u03b1\u03b8\u03bc\u03bf\u03bd\u03bf\u03bc\u03ae\u03c3\u03c4\u03b5 \u03c4\u03b7\u03bd \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf score<\/strong>\n<p>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac thresholds \u03b3\u03b9\u03b1 auto-hide, human review \u03ba\u03b1\u03b9 allow, \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 reject option \u03cc\u03c4\u03b1\u03bd \u03b7 \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ae \u03c4\u03bf distribution shift \u03b5\u03af\u03bd\u03b1\u03b9 \u03c5\u03c8\u03b7\u03bb\u03ac.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 6<\/span><strong>\u0394\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b5 reviewer flow \u03ba\u03b1\u03b9 appeals<\/strong>\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c7\u03c1\u03cc\u03bd\u03bf \u03b1\u03bd\u03b1\u03b8\u03b5\u03ce\u03c1\u03b7\u03c3\u03b7\u03c2, \u03c3\u03c5\u03bc\u03c6\u03c9\u03bd\u03af\u03b1, overturn rate \u03ba\u03b1\u03b9 \u03c0\u03c1\u03cc\u03c3\u03b2\u03b1\u03c3\u03b7 \u03c3\u03b5 context \u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf \u03b2\u03ae\u03bc\u03b1 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc control \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b4\u03b9\u03b1\u03ba\u03bf\u03c3\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 7<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 monitoring, rollback \u03ba\u03b1\u03b9 retraining gate<\/strong>\n<p>\u03a0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03ae\u03c3\u03c4\u03b5 drift, \u03bd\u03ad\u03b1 \u03b1\u03c1\u03b3\u03ba\u03cc, false positives \u03ba\u03b1\u03b9 incidents \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c8\u03c4\u03b5 \u03bd\u03ad\u03bf adapter \u03bc\u03cc\u03bd\u03bf \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc regression suite \u03ba\u03b1\u03b9 \u03c5\u03c0\u03b5\u03cd\u03b8\u03c5\u03bd\u03b7 \u03ad\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<p>\u03a4\u03bf pilot \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03b1\u03c4\u03b1\u03bb\u03ae\u03b3\u03b5\u03b9 \u03c3\u03b5 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03bc\u03b5 \u03cc\u03c1\u03bf\u03c5\u03c2: \u03c0\u03bf\u03b9\u03bf use case \u03b5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9, \u03c0\u03bf\u03b9\u03b1 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1 \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03c4\u03b1\u03b9, \u03c0\u03bf\u03b9\u03b1 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7, \u03c0\u03bf\u03b9\u03b1 thresholds \u03b9\u03c3\u03c7\u03cd\u03bf\u03c5\u03bd \u03ba\u03b1\u03b9 \u03c0\u03cc\u03c4\u03b5 \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03b5\u03c0\u03b9\u03c3\u03c4\u03c1\u03ad\u03c6\u03b5\u03b9 \u03c3\u03b5 manual review. \u0391\u03c5\u03c4\u03bf\u03af \u03bf\u03b9 \u03cc\u03c1\u03bf\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b9\u03bf \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf\u03b9 \u03b1\u03c0\u03cc \u03bc\u03b9\u03b1 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03ba\u03ae\u03c1\u03c5\u03be\u03b7 \u03cc\u03c4\u03b9 \u00ab\u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 94%\u00bb.<\/p>\n<h2 id=\"teliko-symperasma\">\u03a4\u03bf \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b3\u03b9\u03b1 low-resource \u03b3\u03bb\u03ce\u03c3\u03c3\u03b5\u03c2<\/h2>\n<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03c4\u03b7\u03c2 Roman Urdu \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03b7 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03ad\u03c7\u03b5\u03b9 \u03b1\u03be\u03af\u03b1. \u03a4\u03bf direct inference \u03b4\u03b5\u03bd \u03ad\u03b4\u03c9\u03c3\u03b5 \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03b7 \u03b9\u03c3\u03bf\u03c1\u03c1\u03bf\u03c0\u03af\u03b1, \u03c4\u03bf few-shot prompting \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b5 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b1 \u03c4\u03b7\u03bd \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03c7\u03c9\u03c1\u03af\u03c2 training \u03ba\u03b1\u03b9 \u03c4\u03bf LoRA \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 \u03c4\u03b1 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b1 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03b1 F1 \u03bc\u03b5 \u03bc\u03b9\u03ba\u03c1\u03cc trainable footprint \u03c3\u03b5 \u03c3\u03c7\u03ad\u03c3\u03b7 \u03bc\u03b5 \u03c4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf.<\/p>\n<p>\u03a4\u03bf \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03cc \u03bc\u03ae\u03bd\u03c5\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u00ab\u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03c0\u03ac\u03bd\u03c4\u03b1 LoRA\u00bb. \u0395\u03af\u03bd\u03b1\u03b9 \u00ab\u03bc\u03b7\u03bd \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b5 moderation \u03c3\u03b5 \u03bc\u03b9\u03b1 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1 \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03ad\u03c7\u03b5\u03c4\u03b5 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03b5\u03b9 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ac\u00bb. \u0397 adapter-based \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03b9\u03b1 \u03cc\u03c4\u03b1\u03bd \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03b5\u03c0\u03b1\u03c1\u03ba\u03ad\u03c2, \u03bd\u03cc\u03bc\u03b9\u03bc\u03bf \u03ba\u03b1\u03b9 \u03b1\u03bd\u03c4\u03b9\u03c0\u03c1\u03bf\u03c3\u03c9\u03c0\u03b5\u03c5\u03c4\u03b9\u03ba\u03cc corpus. \u03a4\u03bf prompting \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03b3\u03b9\u03b1 baseline, \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b7 \u03b4\u03b9\u03b5\u03c1\u03b5\u03cd\u03bd\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2 \u03cc\u03c0\u03bf\u03c5 \u03c4\u03bf training \u03b4\u03b5\u03bd \u03b4\u03b9\u03ba\u03b1\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af\u03c4\u03b1\u03b9.<\/p>\n<p>\u039f\u03b9 \u03b1\u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03b9\u03b5\u03c2 \u03c4\u03c9\u03bd \u03c0\u03b9\u03bd\u03ac\u03ba\u03c9\u03bd \u03c5\u03c0\u03b5\u03bd\u03b8\u03c5\u03bc\u03af\u03b6\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03b7 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2. \u03a0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03ae \u03c7\u03c1\u03ae\u03c3\u03b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac splits, \u03c0\u03bb\u03ae\u03c1\u03b5\u03b9\u03c2 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03b9\u03c2, slice metrics, calibration, versioning \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03bf\u03c2 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2. \u039a\u03b1\u03b9 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03ba\u03cd\u03c1\u03c9\u03c3\u03b7 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03ac\u03bd\u03b8\u03c1\u03c9\u03c0\u03bf\u03c2, \u03b1\u03b9\u03c4\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7, \u03b4\u03b9\u03ba\u03b1\u03af\u03c9\u03bc\u03b1 appeal \u03ba\u03b1\u03b9 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b1\u03bd\u03ac\u03ba\u03bb\u03b7\u03c3\u03b7\u03c2.<\/p>\n<p>\u0388\u03c4\u03c3\u03b9 \u03b7 AI \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b2\u03bf\u03ae\u03b8\u03b7\u03bc\u03b1 \u03b3\u03b9\u03b1 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c3\u03c4\u03b5\u03c1\u03b5\u03c2 \u03c8\u03b7\u03c6\u03b9\u03b1\u03ba\u03ad\u03c2 \u03ba\u03bf\u03b9\u03bd\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03b1\u03b4\u03b9\u03b1\u03c6\u03b1\u03bd\u03ae \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc \u03c3\u03af\u03b3\u03b1\u03c3\u03b7\u03c2. \u0397 \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03b7 moderation \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf classification problem\u00b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03cc\u03c2 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1\u03c2, \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae\u03c2, \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, UX \u03ba\u03b1\u03b9 \u03bb\u03bf\u03b3\u03bf\u03b4\u03bf\u03c3\u03af\u03b1\u03c2.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-kicker\">\u0391\u03c0\u03cc \u03c4\u03bf model score \u03c3\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b7 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1<\/p>\n<p class=\"td-service-cta-title\">\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 \u03ad\u03bd\u03b1 AI moderation pilot \u03bc\u03b5 \u03c3\u03b1\u03c6\u03ae \u03cc\u03c1\u03b9\u03b1<\/p>\n<p>\u0397 TWO DOTS \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03b5\u03af content flows, labels, integrations, quality gates, reviewer approvals, audit trail \u03ba\u03b1\u03b9 monitoring \u03ce\u03c3\u03c4\u03b5 \u03b7 \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03bd\u03b1 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03c0\u03b1\u03af\u03c1\u03bd\u03b5\u03b9 \u03b1\u03bd\u03b5\u03be\u03ad\u03bb\u03b5\u03b3\u03ba\u03c4\u03b5\u03c2 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b3\u03b9\u03b1 \u03c0\u03b5\u03bb\u03ac\u03c4\u03b5\u03c2 \u03ae \u03ba\u03bf\u03b9\u03bd\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u03a3\u03c5\u03b6\u03b7\u03c4\u03ae\u03c3\u03c4\u03b5 \u03ad\u03bd\u03b1 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf AI workflow<\/a><\/div>\n<\/div>\n<\/section>\n<section id=\"sychnes-erotiseis\" class=\"td-faq-section\">\n<div class=\"td-faq\">\n<p class=\"td-faq-heading\">\u0427\u0435\u0441\u0442\u043e \u0437\u0430\u0434\u0430\u0432\u0430\u043d\u0438 \u0432\u044a\u043f\u0440\u043e\u0441\u0438<\/p>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b3\u03b9\u03b1 \u03c4\u03b7 Roman Urdu;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 direct inference, LoRA-based PEFT, prompt tuning \u03c3\u03b5 multilingual BERT \u03ba\u03b1\u03b9 zero-shot \u03ae few-shot prompting \u03bc\u03b5 GPT-3.5 \u03b3\u03b9\u03b1 binary toxic\/non-toxic \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 Roman Urdu \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03cd\u03c3\u03ba\u03bf\u03bb\u03b7 \u03b3\u03b9\u03b1 content moderation;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0394\u03b5\u03bd \u03ad\u03c7\u03b5\u03b9 \u03b5\u03bd\u03b9\u03b1\u03af\u03b1 \u03bf\u03c1\u03b8\u03bf\u03b3\u03c1\u03b1\u03c6\u03af\u03b1, \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03b9 \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03c6\u03c9\u03bd\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b3\u03c1\u03b1\u03c6\u03ad\u03c2, \u03c3\u03b7\u03bc\u03b1\u03c3\u03b9\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03b1\u03bc\u03c6\u03b9\u03c3\u03b7\u03bc\u03af\u03b1 \u03ba\u03b1\u03b9 \u03c3\u03c5\u03c7\u03bd\u03cc code-switching, \u03bf\u03c0\u03cc\u03c4\u03b5 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03bd\u03cc\u03b7\u03bc\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03c6\u03b1\u03bd\u03b5\u03b9\u03b1\u03ba\u03ae \u03bc\u03bf\u03c1\u03c6\u03ae.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03bf dataset \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b5;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b7 Roman Urdu \u03c0\u03bb\u03b5\u03c5\u03c1\u03ac \u03c4\u03bf\u03c5 PURUTT, \u03bc\u03b5 72.771 \u03c3\u03c7\u03cc\u03bb\u03b9\u03b1: 13.097 toxic \u03ba\u03b1\u03b9 59.674 non-toxic. \u0397 \u03b4\u03b9\u03b1\u03c4\u03c1\u03b9\u03b2\u03ae \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf corpus \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03b7\u03bc\u03cc\u03c3\u03b9\u03bf.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03b1 \u03ae\u03c4\u03b1\u03bd \u03b7 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf Mistral-7B-v0.3 \u03bc\u03b5 LoRA \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 F1 0,9387 \u03ba\u03b1\u03b9 accuracy 0,9642, \u03c0\u03bf\u03bb\u03cd \u03c0\u03ac\u03bd\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf direct-inference baseline \u03c4\u03bf\u03c5 \u03af\u03b4\u03b9\u03bf\u03c5 model.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03ce\u03c2 \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c4\u03bf LoRA \u03b1\u03c0\u03cc \u03c4\u03bf prompting;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf prompting \u03ba\u03b1\u03b8\u03bf\u03b4\u03b7\u03b3\u03b5\u03af \u03c4\u03bf \u03c5\u03c0\u03ac\u03c1\u03c7\u03bf\u03bd model \u03c7\u03c9\u03c1\u03af\u03c2 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae weights, \u03b5\u03bd\u03ce \u03c4\u03bf LoRA \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03b5\u03b9 \u03bc\u03b9\u03ba\u03c1\u03bf\u03cd\u03c2 adapters \u03ba\u03b1\u03b9 \u03ba\u03c1\u03b1\u03c4\u03ac \u03c0\u03b1\u03b3\u03c9\u03bc\u03ad\u03bd\u03bf \u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 backbone.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0389\u03c4\u03b1\u03bd \u03b1\u03c0\u03bf\u03c4\u03c5\u03c7\u03b7\u03bc\u03ad\u03bd\u03bf \u03c4\u03bf few-shot prompting;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0391\u03bd\u03ad\u03b2\u03b1\u03c3\u03b5 \u03c4\u03bf \u03bc\u03ad\u03c3\u03bf F1 \u03c4\u03bf\u03c5 GPT-3.5 \u03b1\u03c0\u03cc 0,6698 \u03c3\u03c4\u03bf zero-shot \u03c3\u03b5 0,8565, \u03b1\u03bb\u03bb\u03ac \u03ad\u03bc\u03b5\u03b9\u03bd\u03b5 \u03ba\u03ac\u03c4\u03c9 \u03b1\u03c0\u03cc \u03c4\u03b1 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03b1 LoRA \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u039c\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03c4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03b5\u03c1\u03b8\u03bf\u03cd\u03bd \u03b1\u03c0\u03b5\u03c5\u03b8\u03b5\u03af\u03b1\u03c2 \u03c3\u03b5 \u03ac\u03bb\u03bb\u03b7 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b1\u03c6\u03bf\u03c1\u03ac Roman Urdu, \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf binary label set \u03ba\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03bc\u03b7 \u03b4\u03b7\u03bc\u03cc\u03c3\u03b9\u03bf corpus. \u039a\u03ac\u03b8\u03b5 \u03bd\u03ad\u03b1 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1, \u03b1\u03b3\u03bf\u03c1\u03ac \u03ba\u03b1\u03b9 policy \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b4\u03b9\u03ba\u03ae \u03c4\u03b7\u03c2 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03c4\u03bf model \u03bd\u03b1 \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03af\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf\u03c5 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b5\u03c2;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf paper \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af classification, \u03cc\u03c7\u03b9 production governance. \u0393\u03b9\u03b1 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b5\u03c2 \u03c5\u03c8\u03b7\u03bb\u03bf\u03cd \u03b1\u03bd\u03c4\u03af\u03ba\u03c4\u03c5\u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 calibrated thresholds, \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf\u03c2 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2, \u03b1\u03b9\u03c4\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7, appeals \u03ba\u03b1\u03b9 audit trail.<\/p>\n<\/div>\n<\/details>\n<\/div>\n<\/section>\n<div class=\"td-source-list\">\n<p id=\"piges\" class=\"td-source-list-title\">\u0418\u0437\u0442\u043e\u0447\u043d\u0438\u0446\u0438<\/p>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2608.21408\" target=\"_blank\" rel=\"noopener\">arXiv \u2014 Hate Speech Classification in Roman Urdu: A Comparative Study on Parameter Efficient Fine-Tuning and Prompt Engineering<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2608.18142\" target=\"_blank\" rel=\"noopener\">arXiv \u2014 Efficient Adaptation of LLMs for Hate Speech Detection in Low-Resource Languages<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/10856102\/\" target=\"_blank\" rel=\"noopener\">IEEE Access \u2014 Urdu Toxic Comment Classification With PURUTT Corpus Development<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2106.09685\" target=\"_blank\" rel=\"noopener\">arXiv \u2014 LoRA: Low-Rank Adaptation of Large Language Models<\/a><\/li>\n<li><a href=\"https:\/\/airc.nist.gov\/airmf-resources\/airmf\/\" target=\"_blank\" rel=\"noopener\">NIST \u2014 AI Risk Management Framework<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u0397 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 LoRA, prompt tuning \u03ba\u03b1\u03b9 few-shot \u03c3\u03c4\u03b7 Roman Urdu \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b9\u03c3\u03c7\u03c5\u03c1\u03ae \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7, \u03b1\u03bb\u03bb\u03ac \u03ba\u03b1\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b1 \u03ba\u03b5\u03bd\u03ac \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc production moderation.<\/p>","protected":false},"author":1,"featured_media":98206,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[199],"tags":[20234,19275,20474,7456,20473],"class_list":["post-97201","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-techniti-noimosyni","tag-content-moderation","tag-lora","tag-peft","tag-prompt-engineering","tag-roman-urdu"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"_links":{"self":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/97201","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/comments?post=97201"}],"version-history":[{"count":0,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/97201\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media\/98206"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media?parent=97201"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/categories?post=97201"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/tags?post=97201"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}