{"id":87071,"date":"2026-07-28T17:39:11","date_gmt":"2026-07-28T14:39:11","guid":{"rendered":"https:\/\/twodots.gr\/?p=87071"},"modified":"2026-07-28T17:39:13","modified_gmt":"2026-07-28T14:39:13","slug":"sofia-plithous-llms-provlepoun-kalytera-mazi","status":"publish","type":"post","link":"https:\/\/twodots.gr\/bg\/sofia-plithous-llms-provlepoun-kalytera-mazi\/","title":{"rendered":"\u0397 \u03c3\u03bf\u03c6\u03af\u03b1 \u03c4\u03bf\u03c5 \u03c0\u03bb\u03ae\u03b8\u03bf\u03c5\u03c2 \u03c4\u03c9\u03bd LLMs: \u03c0\u03cc\u03c4\u03b5 \u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 AI \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03bf\u03c5\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03bc\u03b1\u03b6\u03af"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>\u03a4\u03b1 LLM ensembles \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03bf\u03c5\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03b1\u03c0\u03cc \u03ba\u03ac\u03b8\u03b5 \u03bc\u03b5\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03cc\u03c4\u03b1\u03bd \u03bf \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03cc\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03bc\u03bf\u03c4\u03af\u03b2\u03b1 \u03bb\u03ac\u03b8\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ac \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1.<\/strong> \u0397 \u03b1\u03c0\u03bb\u03ae \u03c0\u03bb\u03b5\u03b9\u03bf\u03c8\u03b7\u03c6\u03af\u03b1 \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af: \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2, \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03ad\u03c2 \u03b5\u03ba\u03b2\u03ac\u03c3\u03b5\u03b9\u03c2, \u03c3\u03c9\u03c3\u03c4\u03cc\u03c2 aggregator \u03ba\u03b1\u03b9 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 \u03ce\u03c3\u03c4\u03b5 \u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03bd\u03b1 \u03bc\u03b7\u03bd \u00ab\u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bd\u00bb \u03ae\u03b4\u03b7 \u03c4\u03b7\u03bd \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03ae\u03c2 \u03c4\u03bf\u03c5\u03c2.<\/p>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 <em>Wisdom of LLM Crowds: Aggregation and Contamination in Language Model Ensembles<\/em> \u03c3\u03c5\u03b3\u03ba\u03ad\u03bd\u03c4\u03c1\u03c9\u03c3\u03b5 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03b9\u03c2 \u03b1\u03c0\u03cc 15 LLMs \u03b3\u03b9\u03b1 254 \u03b4\u03c5\u03b1\u03b4\u03b9\u03ba\u03ac \u03b5\u03c1\u03c9\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1. \u03a3\u03c4\u03b1 94 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ac \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac \u03b5\u03c1\u03c9\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1, \u03b7 logistic regression \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 Brier score 0,241, \u03c4\u03bf MLP 0,264 \u03ba\u03b1\u03b9 \u03bf \u03b1\u03c0\u03bb\u03cc\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc\u03c2 \u03bc\u03ad\u03c3\u03bf\u03c2 0,313. \u03a4\u03bf \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\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\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1\u00bb, \u03b1\u03bb\u03bb\u03ac \u00ab\u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c0\u03bf\u03b9\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03bf\u03c5\u03bd \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03c3\u03ae\u03bc\u03b1\u00bb.<\/p>\n<\/div>\n<div class=\"td-article-note\">\n<p><strong>\u03a4\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7:<\/strong> \u03c4\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 weights, \u03c4\u03b1 \u03b6\u03b5\u03cd\u03b3\u03b7 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03b5\u03c0\u03b9\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03b4\u03b5\u03bd \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c5\u03c4\u03bf\u03bc\u03ac\u03c4\u03c9\u03c2 \u03c3\u03b5 forecasting \u03b6\u03ae\u03c4\u03b7\u03c3\u03b7\u03c2, risk scoring, marketing \u03ae e-commerce. \u039a\u03ac\u03b8\u03b5 \u03c7\u03c1\u03ae\u03c3\u03b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b4\u03b9\u03ba\u03ac \u03c4\u03b7\u03c2 resolved cases, \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03cc train\/test split, \u03c0\u03c1\u03bf\u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf metric \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03ae prompt.<\/p>\n<\/div>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">\u03a0\u03b5\u03c1\u03b9\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03b1<\/div>\n<ul>\n<li><a href=\"#sofia-plithous-llm-ensembles\">\u0391\u03c0\u03cc \u03c4\u03b7 \u03c3\u03bf\u03c6\u03af\u03b1 \u03c4\u03c9\u03bd \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03c9\u03bd \u03bf\u03bc\u03ac\u03b4\u03c9\u03bd \u03c3\u03c4\u03b1 ensembles \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae\u03c2 \u03bd\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2<\/a><\/li>\n<li><a href=\"#schediasmos-meletis-chroniki-diastasi\">\u03a0\u03ce\u03c2 \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5 \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03ba\u03b1\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03b7 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ae \u03b4\u03b9\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7<\/a><\/li>\n<li><a href=\"#diadikasia-provlepsis-choris-web-search\">\u0397 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7\u03c2: \u03af\u03b4\u03b9\u03b1 \u03b5\u03c1\u03ce\u03c4\u03b7\u03c3\u03b7, \u03af\u03b4\u03b9\u03b1 \u03bc\u03bf\u03c1\u03c6\u03ae, \u03c7\u03c9\u03c1\u03af\u03c2 web search<\/a><\/li>\n<li><a href=\"#mesos-oros-learned-aggregation\">\u0391\u03c0\u03cc \u03c4\u03bf\u03bd \u03b1\u03c0\u03bb\u03cc \u03bc\u03ad\u03c3\u03bf \u03cc\u03c1\u03bf \u03c3\u03c4\u03b7 learned aggregation<\/a><\/li>\n<li><a href=\"#metriseis-aggregators\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03bf\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03bf\u03c5\u03c2 aggregators<\/a><\/li>\n<li><a href=\"#adynamo-montelo-axia-omada\">\u0393\u03b9\u03b1\u03c4\u03af \u03ad\u03bd\u03b1 \u00ab\u03b1\u03b4\u03cd\u03bd\u03b1\u03bc\u03bf\u00bb \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03bf\u03bb\u03cd\u03c4\u03b9\u03bc\u03bf \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03bf\u03bc\u03ac\u03b4\u03b1<\/a><\/li>\n<li><a href=\"#contamination-katataxi-montelon\">\u03a4\u03bf contamination \u03ac\u03bb\u03bb\u03b1\u03be\u03b5 \u03b4\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03c4\u03b7\u03bd \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03c4\u03c9\u03bd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd<\/a><\/li>\n<li><a href=\"#llm-crowds-anthropini-agora\">\u03a4\u03b1 LLM crowds \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03c6\u03c4\u03ac\u03bd\u03bf\u03c5\u03bd \u03c4\u03b7 \u03b4\u03c5\u03bd\u03b1\u03bc\u03b9\u03ba\u03ae \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03b1\u03b3\u03bf\u03c1\u03ac<\/a><\/li>\n<li><a href=\"#epicheiriseis-marketers-ai-workflows\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2, marketers \u03ba\u03b1\u03b9 AI workflows<\/a><\/li>\n<li><a href=\"#asfalestero-plaisio-multi-model-apofaseis\">\u0388\u03bd\u03b1 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c3\u03c4\u03b5\u03c1\u03bf \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03b3\u03b9\u03b1 multi-model \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2<\/a><\/li>\n<li><a href=\"#periorismoi-genikefseon\">\u039f\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03bf\u03c5\u03bd \u03b5\u03cd\u03ba\u03bf\u03bb\u03b5\u03c2 \u03b3\u03b5\u03bd\u03b9\u03ba\u03b5\u03cd\u03c3\u03b5\u03b9\u03c2<\/a><\/li>\n<li><a href=\"#ousia-diaforetikotita-lathon\">\u0397 \u03bf\u03c5\u03c3\u03af\u03b1: \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bb\u03b1\u03b8\u03ce\u03bd, \u03ba\u03b1\u03b8\u03b1\u03c1\u03cc\u03c2 \u03c7\u03c1\u03cc\u03bd\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03b7\u03bc\u03ad\u03bd\u03b7 \u03b5\u03bc\u03c0\u03b9\u03c3\u03c4\u03bf\u03c3\u03cd\u03bd\u03b7<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"sofia-plithous-llm-ensembles\">\u0391\u03c0\u03cc \u03c4\u03b7 \u03c3\u03bf\u03c6\u03af\u03b1 \u03c4\u03c9\u03bd \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03c9\u03bd \u03bf\u03bc\u03ac\u03b4\u03c9\u03bd \u03c3\u03c4\u03b1 ensembles \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae\u03c2 \u03bd\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2<\/h2>\n<p>\u03a3\u03c4\u03b9\u03c2 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b5\u03c2 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2, \u03b7 \u03ba\u03bb\u03b1\u03c3\u03b9\u03ba\u03ae \u03b5\u03be\u03ae\u03b3\u03b7\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03c3\u03c5\u03bb\u03bb\u03bf\u03b3\u03b9\u03ba\u03ae\u03c2 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03bf\u03b9 \u03b5\u03c0\u03b9\u03bc\u03ad\u03c1\u03bf\u03c5\u03c2 \u03b5\u03ba\u03c4\u03b9\u03bc\u03b7\u03c4\u03ad\u03c2 \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf\u03c5 \u03c3\u03ae\u03bc\u03b1\u03c4\u03bf\u03c2, \u03b1\u03bb\u03bb\u03ac \u03ba\u03ac\u03bd\u03bf\u03c5\u03bd \u03b5\u03bd \u03bc\u03ad\u03c1\u03b5\u03b9 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b1 \u03bb\u03ac\u03b8\u03b7. \u038c\u03c4\u03b1\u03bd \u03bf\u03b9 \u03b5\u03ba\u03c4\u03b9\u03bc\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9, \u03c4\u03b1 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03bb\u03ac\u03b8\u03b7 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b1\u03bb\u03bb\u03b7\u03bb\u03bf\u03b1\u03bd\u03b1\u03b9\u03c1\u03b5\u03b8\u03bf\u03cd\u03bd, \u03b5\u03bd\u03ce \u03c4\u03bf \u03ba\u03bf\u03b9\u03bd\u03cc \u03c3\u03ae\u03bc\u03b1 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af\u03c4\u03b1\u03b9. \u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03c0\u03bb\u03ae\u03b8\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03bf\u03c6\u03cc \u03bf\u03cd\u03c4\u03b5 \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1\u03c2 \u03b1\u03c0\u03bb\u03cc\u03c2 \u03bc\u03ad\u03c3\u03bf\u03c2 \u03cc\u03c1\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03ac\u03bd\u03c4\u03b1 \u03b7 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03bb\u03cd\u03c3\u03b7. \u03a3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03c0\u03bf\u03b9\u03ba\u03b9\u03bb\u03af\u03b1 \u03c4\u03c9\u03bd \u03c3\u03c6\u03b1\u03bb\u03bc\u03ac\u03c4\u03c9\u03bd \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03b9 \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b9\u03bc\u03bf \u03c0\u03b5\u03c1\u03b9\u03bf\u03c5\u03c3\u03b9\u03b1\u03ba\u03cc \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03bf.<\/p>\n<p>\u0397 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03b1\u03c5\u03c4\u03ae\u03c2 \u03c4\u03b7\u03c2 \u03b9\u03b4\u03ad\u03b1\u03c2 \u03c3\u03c4\u03b1 LLMs \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c5\u03c4\u03bf\u03bd\u03cc\u03b7\u03c4\u03b7. \u03a0\u03bf\u03bb\u03bb\u03ac \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ad\u03c7\u03bf\u03c5\u03bd \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03c4\u03b5\u03af \u03c3\u03b5 \u03b5\u03c0\u03b9\u03ba\u03b1\u03bb\u03c5\u03c0\u03c4\u03cc\u03bc\u03b5\u03bd\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03bf\u03cd\u03bd \u03c0\u03b1\u03c1\u03cc\u03bc\u03bf\u03b9\u03b5\u03c2 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c5\u03b8\u03c5\u03b3\u03c1\u03ac\u03bc\u03bc\u03b9\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03b3\u03bf\u03c5\u03bd \u03c4\u03b1 \u03af\u03b4\u03b9\u03b1 \u03bc\u03bf\u03c4\u03af\u03b2\u03b1 \u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03ae \u03c0\u03b1\u03c1\u03b1\u03bd\u03cc\u03b7\u03c3\u03b7\u03c2. \u0391\u03c0\u03cc \u03c4\u03b7\u03bd \u03ac\u03bb\u03bb\u03b7 \u03c0\u03bb\u03b5\u03c5\u03c1\u03ac, \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03c9\u03c2 \u03c0\u03c1\u03bf\u03c2 \u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae, \u03c4\u03bf\u03bd \u03c0\u03ac\u03c1\u03bf\u03c7\u03bf, \u03c4\u03bf \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2, \u03c4\u03bf training cutoff \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 fine-tuning. \u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03c1\u03c9\u03c4\u03ac \u03b1\u03bd \u03b1\u03c5\u03c4\u03ad\u03c2 \u03bf\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2 \u03b1\u03c1\u03ba\u03bf\u03cd\u03bd \u03ce\u03c3\u03c4\u03b5 \u03bc\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b1 LLMs \u03bd\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b1\u03c0\u03cc \u03bf\u03c0\u03bf\u03b9\u03bf\u03b4\u03ae\u03c0\u03bf\u03c4\u03b5 \u03bc\u03ad\u03bb\u03bf\u03c2 \u03c4\u03b7\u03c2.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7, \u03c4\u03bf \u03b5\u03c1\u03ce\u03c4\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc. \u038c\u03c4\u03b1\u03bd \u03bc\u03b9\u03b1 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03b1\u03c6\u03bf\u03c1\u03ac \u03b6\u03ae\u03c4\u03b7\u03c3\u03b7, \u03c1\u03af\u03c3\u03ba\u03bf, \u03c4\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b1\u03b3\u03bf\u03c1\u03ac\u03c2 \u03ae \u03c4\u03b7\u03bd \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b5\u03c0\u03b9\u03c4\u03c5\u03c7\u03af\u03b1\u03c2 \u03bc\u03b9\u03b1\u03c2 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1\u03c2, \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03b5\u03bb\u03b5\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03bd\u03b1 \u03b6\u03b7\u03c4\u03b7\u03b8\u03b5\u03af \u03b3\u03bd\u03ce\u03bc\u03b7 \u03b1\u03c0\u03cc \u03c0\u03bf\u03bb\u03bb\u03ac \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b9\u03bb\u03b5\u03b3\u03b5\u03af \u03b7 \u03c0\u03bb\u03b5\u03b9\u03bf\u03c8\u03b7\u03c6\u03af\u03b1. \u0397 \u03ad\u03c1\u03b5\u03c5\u03bd\u03b1 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03b1\u03be\u03af\u03b1 \u03b4\u03b5\u03bd \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03c3\u03c4\u03bf\u03bd \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03c4\u03c9\u03bd \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03c9\u03bd. \u0392\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7 \u03b4\u03bf\u03bc\u03ae \u03c4\u03c9\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ce\u03bd \u03c4\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf \u03c0\u03bf\u03c5 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03b4\u03b5\u03ba\u03b1\u03c0\u03ad\u03bd\u03c4\u03b5 \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03c3\u03b5 \u03bc\u03af\u03b1.<\/p>\n<h2 id=\"schediasmos-meletis-chroniki-diastasi\">\u03a0\u03ce\u03c2 \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5 \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03ba\u03b1\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03b7 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ae \u03b4\u03b9\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7<\/h2>\n<p>\u039f\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c1\u03c9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03c1\u03bf\u03ae\u03bb\u03b8\u03b1\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf Manifold Markets, \u03bc\u03b9\u03b1 \u03c0\u03bb\u03b1\u03c4\u03c6\u03cc\u03c1\u03bc\u03b1 prediction market \u03bc\u03b5 \u03b5\u03b9\u03ba\u03bf\u03bd\u03b9\u03ba\u03cc \u03c7\u03c1\u03ae\u03bc\u03b1. \u0395\u03c0\u03b9\u03bb\u03ad\u03c7\u03b8\u03b7\u03ba\u03b1\u03bd \u03b1\u03b3\u03bf\u03c1\u03ad\u03c2 \u03bc\u03b5 \u03b4\u03c5\u03b1\u03b4\u03b9\u03ba\u03ae \u03ad\u03ba\u03b2\u03b1\u03c3\u03b7, YES \u03ae NO, \u03c0\u03bf\u03c5 \u03b5\u03af\u03c7\u03b1\u03bd \u03c3\u03c5\u03b3\u03ba\u03b5\u03bd\u03c4\u03c1\u03ce\u03c3\u03b5\u03b9 \u03c4\u03bf\u03c5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03bf\u03bd 75 \u03bc\u03bf\u03bd\u03b1\u03b4\u03b9\u03ba\u03bf\u03cd\u03c2 traders. \u0395\u03be\u03b1\u03b9\u03c1\u03ad\u03b8\u03b7\u03ba\u03b1\u03bd \u03b5\u03c1\u03c9\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03bc\u03c6\u03af\u03c3\u03b7\u03bc\u03b1, \u03c0\u03c1\u03bf\u03c3\u03c9\u03c0\u03b9\u03ba\u03ac \u03ae \u03b5\u03be\u03b1\u03c1\u03c4\u03b7\u03bc\u03ad\u03bd\u03b1 \u03b1\u03c0\u03cc \u03b5\u03c3\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ae \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b8\u03b1 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b5 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c4\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u03a4\u03bf \u03b1\u03c1\u03c7\u03b9\u03ba\u03cc \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03c0\u03b5\u03c1\u03b9\u03bb\u03ac\u03bc\u03b2\u03b1\u03bd\u03b5 208 \u03b4\u03b7\u03bb\u03ce\u03c3\u03b5\u03b9\u03c2: 107 \u03b1\u03bb\u03b7\u03b8\u03b5\u03af\u03c2 \u03ba\u03b1\u03b9 101 \u03c8\u03b5\u03c5\u03b4\u03b5\u03af\u03c2.<\/p>\n<p>\u0397 \u03ba\u03cd\u03c1\u03b9\u03b1 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ae \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03b2\u03b1\u03c3\u03af\u03c3\u03c4\u03b7\u03ba\u03b5 \u03c3\u03b5 \u03b5\u03c1\u03c9\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03c0\u03bf\u03c5 \u03b5\u03c0\u03b9\u03bb\u03cd\u03b8\u03b7\u03ba\u03b1\u03bd \u03bc\u03b5\u03c4\u03ac \u03c4\u03b7\u03bd 1\u03b7 \u03a3\u03b5\u03c0\u03c4\u03b5\u03bc\u03b2\u03c1\u03af\u03bf\u03c5 2025, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf \u03c0\u03b9\u03bf \u03c0\u03c1\u03cc\u03c3\u03c6\u03b1\u03c4\u03bf training cutoff \u03bc\u03b5\u03c4\u03b1\u03be\u03cd \u03c4\u03c9\u03bd 15 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd. \u0391\u03c0\u03cc \u03c4\u03bf \u03b1\u03c1\u03c7\u03b9\u03ba\u03cc \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf, 48 \u03b5\u03c1\u03c9\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03c0\u03bb\u03b7\u03c1\u03bf\u03cd\u03c3\u03b1\u03bd \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7\u03bd \u03c0\u03c1\u03bf\u03cb\u03c0\u03cc\u03b8\u03b5\u03c3\u03b7. \u03a0\u03c1\u03bf\u03c3\u03c4\u03ad\u03b8\u03b7\u03ba\u03b1\u03bd \u03b1\u03ba\u03cc\u03bc\u03b7 46 \u03bc\u03b5 \u03c4\u03b1 \u03af\u03b4\u03b9\u03b1 \u03ba\u03c1\u03b9\u03c4\u03ae\u03c1\u03b9\u03b1, \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03ce\u03bd\u03c4\u03b1\u03c2 clean subset 94 \u03b5\u03c1\u03c9\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd, 44 \u03bc\u03b5 \u03ad\u03ba\u03b2\u03b1\u03c3\u03b7 YES \u03ba\u03b1\u03b9 50 \u03bc\u03b5 \u03ad\u03ba\u03b2\u03b1\u03c3\u03b7 NO. \u0388\u03c4\u03c3\u03b9, \u03c4\u03b1 254 \u03bc\u03bf\u03bd\u03b1\u03b4\u03b9\u03ba\u03ac \u03b5\u03c1\u03c9\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03bc\u03bf\u03b9\u03c1\u03ac\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03ac \u03c3\u03b5 \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b3\u03b9\u03b1 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7 \u03c4\u03c9\u03bd learned aggregators \u03ba\u03b1\u03b9 \u03c3\u03b5 \u03ba\u03b1\u03b8\u03b1\u03c1\u03cc, \u03bc\u03b5\u03c4\u03b1\u03b3\u03b5\u03bd\u03ad\u03c3\u03c4\u03b5\u03c1\u03bf \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03b3\u03b9\u03b1 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ae.<\/p>\n<p>\u03a4\u03b1 15 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ba\u03ac\u03bb\u03c5\u03c0\u03c4\u03b1\u03bd \u03c0\u03ad\u03bd\u03c4\u03b5 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ac \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03bc\u03ad\u03c3\u03c9 Ollama \u03ba\u03b1\u03b9 \u03b4\u03ad\u03ba\u03b1 cloud \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03b1\u03c0\u03cc Anthropic, OpenAI, Google \u03ba\u03b1\u03b9 DeepSeek. \u03a0\u03b5\u03c1\u03b9\u03bb\u03ac\u03bc\u03b2\u03b1\u03bd\u03b1\u03bd, \u03bc\u03b5\u03c4\u03b1\u03be\u03cd \u03ac\u03bb\u03bb\u03c9\u03bd, Mistral 7B, LLaMA 3.1 8B, Gemma 2 9B, Phi-4 14B, Qwen 2.5 7B, \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 GPT, Gemini \u03ba\u03b1\u03b9 Claude. \u0397 \u03c0\u03b7\u03b3\u03ae \u03b4\u03b7\u03bb\u03ce\u03bd\u03b5\u03b9 \u03c1\u03b7\u03c4\u03ac \u03cc\u03c4\u03b9 \u03c4\u03b1 \u03bc\u03b5\u03b3\u03ad\u03b8\u03b7 \u03c4\u03c9\u03bd cloud \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd \u03b4\u03b5\u03bd \u03ae\u03c4\u03b1\u03bd \u03b4\u03b7\u03bc\u03bf\u03c3\u03af\u03c9\u03c2 \u03b3\u03bd\u03c9\u03c3\u03c4\u03ac \u03ba\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03b1 training cutoffs \u03ae\u03c4\u03b1\u03bd \u03ba\u03b1\u03c4\u03ac \u03c0\u03c1\u03bf\u03c3\u03ad\u03b3\u03b3\u03b9\u03c3\u03b7, \u03b2\u03ac\u03c3\u03b5\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b7\u03c2 \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03af\u03c9\u03c3\u03b7\u03c2 \u03cc\u03c0\u03bf\u03c5 \u03b1\u03c5\u03c4\u03ae \u03ae\u03c4\u03b1\u03bd \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b7.<\/p>\n<h2 id=\"diadikasia-provlepsis-choris-web-search\">\u0397 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7\u03c2: \u03af\u03b4\u03b9\u03b1 \u03b5\u03c1\u03ce\u03c4\u03b7\u03c3\u03b7, \u03af\u03b4\u03b9\u03b1 \u03bc\u03bf\u03c1\u03c6\u03ae, \u03c7\u03c9\u03c1\u03af\u03c2 web search<\/h2>\n<p>\u039a\u03ac\u03b8\u03b5 LLM \u03ad\u03bb\u03b1\u03b2\u03b5 \u03c4\u03c5\u03c0\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03bf prompt. \u0388\u03c0\u03c1\u03b5\u03c0\u03b5 \u03bd\u03b1 \u03b5\u03ba\u03c4\u03b9\u03bc\u03ae\u03c3\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bb\u03b7\u03b8\u03ae\u03c2 \u03bc\u03b9\u03b1 \u03b4\u03ae\u03bb\u03c9\u03c3\u03b7, \u03bd\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b7\u03bc\u03b5\u03c1\u03bf\u03bc\u03b7\u03bd\u03af\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03b9\u03c2 \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ad\u03c2 \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2, \u03bd\u03b1 \u03b2\u03b1\u03c3\u03b9\u03c3\u03c4\u03b5\u03af \u03bc\u03cc\u03bd\u03bf \u03c3\u03c4\u03b7 \u03b3\u03bd\u03ce\u03c3\u03b7 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03ae\u03c2 \u03c4\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03bc\u03b7\u03bd \u03b1\u03bd\u03b1\u03b6\u03b7\u03c4\u03ae\u03c3\u03b5\u03b9 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b5\u03c2 \u03c3\u03c4\u03bf \u03b4\u03b9\u03b1\u03b4\u03af\u03ba\u03c4\u03c5\u03bf. \u0397 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03ad\u03c0\u03c1\u03b5\u03c0\u03b5 \u03bd\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03c4\u03b7 \u03bc\u03bf\u03c1\u03c6\u03ae <code>PROBABILITY: [number]<\/code>, \u03bc\u03b5 \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03b1\u03c0\u03cc 0 \u03ad\u03c9\u03c2 1.<\/p>\n<p>\u0391\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf \u03c6\u03b1\u03b9\u03bd\u03bf\u03bc\u03b5\u03bd\u03b9\u03ba\u03ac \u03b1\u03c0\u03bb\u03cc \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf \u03b1\u03c0\u03bf\u03ba\u03ac\u03bb\u03c5\u03c8\u03b5 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03ad\u03c2 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2. \u03a4\u03b1 Gemini Flash \u03ba\u03b1\u03b9 Gemini Pro \u03b1\u03c1\u03c7\u03b9\u03ba\u03ac \u03b4\u03b5\u03bd \u03ad\u03b4\u03b9\u03bd\u03b1\u03bd \u03b1\u03bd\u03b1\u03b3\u03bd\u03ce\u03c3\u03b9\u03bc\u03b7 \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c3\u03b5 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03cc \u03c4\u03c9\u03bd \u03b5\u03c1\u03c9\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd, \u03b1\u03bb\u03bb\u03ac \u03bc\u03af\u03b1 \u03c3\u03cd\u03bd\u03c4\u03bf\u03bc\u03b7 follow-up \u03b1\u03c0\u03b1\u03af\u03c4\u03b7\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03bc\u03bf\u03c1\u03c6\u03ae \u03b1\u03bd\u03ad\u03ba\u03c4\u03b7\u03c3\u03b5 \u03c4\u03b9\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2. \u03a4\u03bf LLaMA 3.1 8B \u03b1\u03c1\u03bd\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b1\u03c1\u03c7\u03b9\u03ba\u03ac \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ac \u03b5\u03c5\u03b1\u03af\u03c3\u03b8\u03b7\u03c4\u03b5\u03c2 \u03b5\u03c1\u03c9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c3\u03c4\u03bf 22,6% \u03c4\u03c9\u03bd \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03c9\u03bd, \u03ba\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03bf\u03c5\u03b4\u03ad\u03c4\u03b5\u03c1\u03bf system message \u03b3\u03b9\u03b1 \u03b1\u03ba\u03b1\u03b4\u03b7\u03bc\u03b1\u03ca\u03ba\u03ae \u03ac\u03c3\u03ba\u03b7\u03c3\u03b7 calibration \u03b1\u03bd\u03ad\u03ba\u03c4\u03b7\u03c3\u03b5 \u03c4\u03b9\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2. \u03a4\u03bf Mistral 7B \u03c3\u03c5\u03c7\u03bd\u03ac \u03c0\u03b1\u03c1\u03ad\u03bb\u03b5\u03b9\u03c0\u03b5 \u03c4\u03bf \u03b6\u03b7\u03c4\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf tag, \u03b1\u03bb\u03bb\u03ac \u03c3\u03c7\u03b5\u03b4\u03cc\u03bd \u03c0\u03ac\u03bd\u03c4\u03b1 \u03c5\u03c0\u03ae\u03c1\u03c7\u03b5 \u03b4\u03b5\u03ba\u03b1\u03b4\u03b9\u03ba\u03cc\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc\u03c2 \u03c0\u03bf\u03c5 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b5 \u03bd\u03b1 \u03b5\u03be\u03b1\u03c7\u03b8\u03b5\u03af.<\/p>\n<p>\u0391\u03c5\u03c4\u03ad\u03c2 \u03bf\u03b9 \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03ad\u03c1\u03b5\u03b9\u03b5\u03c2 \u03ad\u03c7\u03bf\u03c5\u03bd \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1. \u0388\u03bd\u03b1 multi-model workflow \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ba\u03b1\u03b9 prompts \u03b1\u03bb\u03bb\u03ac \u03ba\u03b1\u03b9 parsing, validation, retry policy \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ae \u03c7\u03b5\u03b9\u03c1\u03b9\u03c3\u03bc\u03cc refusals. \u0394\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac, \u03c4\u03bf \u03c5\u03c0\u03bf\u03c4\u03b9\u03b8\u03ad\u03bc\u03b5\u03bd\u03bf ensemble \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03b5\u03bb\u03bb\u03b9\u03c0\u03ae \u03ae \u03b1\u03c3\u03cd\u03bc\u03b2\u03b1\u03c4\u03b1 outputs \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03c8\u03b5\u03c5\u03b4\u03ae \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03c3\u03c5\u03bd\u03b1\u03af\u03bd\u03b5\u03c3\u03b7\u03c2.<\/p>\n<h2 id=\"mesos-oros-learned-aggregation\">\u0391\u03c0\u03cc \u03c4\u03bf\u03bd \u03b1\u03c0\u03bb\u03cc \u03bc\u03ad\u03c3\u03bf \u03cc\u03c1\u03bf \u03c3\u03c4\u03b7 learned aggregation<\/h2>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c3\u03c5\u03bd\u03ad\u03ba\u03c1\u03b9\u03bd\u03b5 \u03c4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b9\u03c2 \u03ba\u03bb\u03b1\u03c3\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ad\u03c2: arithmetic mean, harmonic mean, geometric\/log-odds mean \u03ba\u03b1\u03b9 median. \u03a3\u03c4\u03b7 \u03b4\u03c5\u03b1\u03b4\u03b9\u03ba\u03ae \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7, \u03bf geometric mean \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc \u03ba\u03b1\u03bd\u03bf\u03bd\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03bf log-odds mean \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bb\u03b3\u03b5\u03b2\u03c1\u03b9\u03ba\u03ac \u03b7 \u03af\u03b4\u03b9\u03b1 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03c2. \u039f \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc\u03c2 \u03bc\u03ad\u03c3\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03c0\u03b9\u03bf \u03ac\u03bc\u03b5\u03c3\u03bf baseline. \u0397 \u03b4\u03b9\u03ac\u03bc\u03b5\u03c3\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03b8\u03b5\u03ba\u03c4\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03b7 \u03c3\u03b5 \u03b1\u03ba\u03c1\u03b1\u03af\u03b5\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2. \u039f log-odds mean \u00ab\u03c3\u03c0\u03c1\u03ce\u03c7\u03bd\u03b5\u03b9\u00bb \u03c3\u03c5\u03c7\u03bd\u03ac \u03c4\u03b7 \u03c3\u03c5\u03b3\u03ba\u03b5\u03bd\u03c4\u03c1\u03c9\u03c4\u03b9\u03ba\u03ae \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c0\u03b9\u03bf \u03bc\u03b1\u03ba\u03c1\u03b9\u03ac \u03b1\u03c0\u03cc \u03c4\u03bf 0,5 \u03ba\u03b1\u03b9 \u03ad\u03c7\u03b5\u03b9 \u03b8\u03b5\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc \u03b5\u03bd\u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd \u03cc\u03c4\u03b1\u03bd \u03bf\u03b9 \u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03bb\u03ac \u03b2\u03b1\u03b8\u03bc\u03bf\u03bd\u03bf\u03bc\u03b7\u03bc\u03ad\u03bd\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03c5\u03c0\u03cc \u03cc\u03c1\u03bf\u03c5\u03c2 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b5\u03c2.<\/p>\n<div class=\"td-comparison\">\n<p class=\"td-comparison-title\">\u0391\u03c0\u03bb\u03ae \u03ba\u03b1\u03b9 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03cc\u03bc\u03b5\u03bd\u03b7 \u03c3\u03c5\u03bd\u03ac\u03b8\u03c1\u03bf\u03b9\u03c3\u03b7<\/p>\n<div class=\"td-comparison-cards td-comparison-cards--horizontal\">\n<div class=\"td-comparison-grid td-comparison-grid--two\">\n<div class=\"td-platform-card\">\n<h3>\u0391\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc\u03c2 \u03bc\u03ad\u03c3\u03bf\u03c2<\/h3>\n<p>\u0394\u03af\u03bd\u03b5\u03b9 \u03af\u03c3\u03bf \u03b2\u03ac\u03c1\u03bf\u03c2 \u03c3\u03b5 \u03cc\u03bb\u03b1 \u03c4\u03b1 LLMs \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af \u03ba\u03b1\u03b8\u03b1\u03c1\u03cc baseline. \u0394\u03b5\u03bd \u03bc\u03b1\u03b8\u03b1\u03af\u03bd\u03b5\u03b9 \u03c0\u03bf\u03b9\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03bf\u03c5\u03bd \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03bb\u03ac\u03b8\u03bf\u03c2 \u03bf\u03cd\u03c4\u03b5 \u03c0\u03bf\u03b9\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03c9\u03c2 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03b1\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03bf \u03c3\u03ae\u03bc\u03b1.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u03a7\u03c9\u03c1\u03af\u03c2 training<\/span><span class=\"td-badge\">\u038a\u03c3\u03b1 weights<\/span><span class=\"td-badge\">Brier 0,313<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<h3>Learned aggregator<\/h3>\n<p>\u039c\u03b1\u03b8\u03b1\u03af\u03bd\u03b5\u03b9 weights \u03b1\u03c0\u03cc \u03c0\u03b1\u03bb\u03b1\u03b9\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b5\u03ba\u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9 \u03b1\u03c3\u03c5\u03c3\u03c7\u03ad\u03c4\u03b9\u03c3\u03c4\u03b1 \u03ae \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03c3\u03c5\u03c3\u03c4\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b1\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b1 \u03bb\u03ac\u03b8\u03b7. \u0391\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03b1\u03c5\u03c3\u03c4\u03b7\u03c1\u03cc temporal split \u03ba\u03b1\u03b9 \u03bd\u03ad\u03bf validation \u03cc\u03c4\u03b1\u03bd \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf ensemble.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u0399\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03ad\u03c2 \u03b5\u03ba\u03b2\u03ac\u03c3\u03b5\u03b9\u03c2<\/span><span class=\"td-badge\">Temporal test<\/span><span class=\"td-badge\">Brier 0,241<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0394\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03b4\u03cd\u03bf \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03b9. \u0397 logistic regression \u03ad\u03bc\u03b1\u03b8\u03b5 \u03ad\u03bd\u03b1\u03bd \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03cc \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03cc \u03c4\u03c9\u03bd 15 \u03c0\u03b9\u03b8\u03b1\u03bd\u03bf\u03c4\u03ae\u03c4\u03c9\u03bd \u03bc\u03b5 L2 regularization. \u03a4\u03bf MLP \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 \u03b5\u03af\u03c3\u03bf\u03b4\u03bf 15 \u03bc\u03bf\u03bd\u03ac\u03b4\u03c9\u03bd, \u03b4\u03cd\u03bf \u03ba\u03c1\u03c5\u03c6\u03ac \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03b1 64 \u03ba\u03b1\u03b9 32 \u03bc\u03bf\u03bd\u03ac\u03b4\u03c9\u03bd \u03bc\u03b5 ReLU \u03ba\u03b1\u03b9 dropout 0,2, \u03ba\u03b1\u03b9 sigmoid output. \u0395\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03c4\u03b7\u03ba\u03b5 \u03b3\u03b9\u03b1 25 epochs \u03bc\u03b5 Adam, learning rate 5\u00d710<sup>\u22123<\/sup>, binary cross-entropy \u03ba\u03b1\u03b9 batch size 16.<\/p>\n<p>\u039a\u03b1\u03b9 \u03bf\u03b9 \u03b4\u03cd\u03bf learned aggregators \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03c4\u03b7\u03ba\u03b1\u03bd \u03c3\u03c4\u03b1 160 \u03c0\u03b1\u03bb\u03b1\u03b9\u03cc\u03c4\u03b5\u03c1\u03b1, \u03bc\u03b7 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac items \u03ba\u03b1\u03b9 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03c3\u03c4\u03b1 94 clean items \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b5\u03af\u03c7\u03b1\u03bd \u03b4\u03b5\u03b9. \u0391\u03c5\u03c4\u03ae \u03b7 temporal transfer \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7 \u03c0\u03c1\u03bf\u03c3\u03b5\u03b3\u03b3\u03af\u03b6\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c7\u03c1\u03ae\u03c3\u03b7: \u03bf aggregator \u03bc\u03b1\u03b8\u03b1\u03af\u03bd\u03b5\u03b9 \u03b1\u03c0\u03cc \u03ae\u03b4\u03b7 \u03b5\u03c0\u03b9\u03bb\u03c5\u03bc\u03ad\u03bd\u03b1 \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03ac \u03b5\u03c1\u03c9\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03bc\u03b5\u03c4\u03b1\u03b3\u03b5\u03bd\u03ad\u03c3\u03c4\u03b5\u03c1\u03b5\u03c2 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03b9\u03c2. \u03a0\u03b1\u03c1\u03ac\u03bb\u03bb\u03b7\u03bb\u03b1, \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03ad\u03bd\u03b1\u03bd \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03cc \u03c0\u03bf\u03c5 \u03bf\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b1\u03bd\u03b1\u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bd: \u03c4\u03b1 weights \u03bc\u03b1\u03b8\u03b1\u03af\u03bd\u03bf\u03c5\u03bd \u03bc\u03ad\u03c3\u03b1 \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd \u03b5\u03c0\u03b7\u03c1\u03b5\u03b1\u03c3\u03bc\u03ad\u03bd\u03bf \u03b1\u03c0\u03cc contamination \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03ac \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03ba\u03b1\u03b8\u03b1\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd.<\/p>\n<h2 id=\"metriseis-aggregators\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03bf\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03bf\u03c5\u03c2 aggregators<\/h2>\n<p>\u03a4\u03bf \u03b2\u03b1\u03c3\u03b9\u03ba\u03cc metric \u03ae\u03c4\u03b1\u03bd \u03c4\u03bf Brier score, \u03cc\u03c0\u03bf\u03c5 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 \u03c4\u03b9\u03bc\u03ae \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03c0\u03b9\u03b8\u03b1\u03bd\u03bf\u03c4\u03b9\u03ba\u03ae \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7. \u03a3\u03c4\u03b1 94 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac \u03b5\u03c1\u03c9\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1, \u03b7 logistic regression \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 Brier score 0,241 \u03ba\u03b1\u03b9 \u03c4\u03bf MLP 0,264. \u039f \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc\u03c2 \u03bc\u03ad\u03c3\u03bf\u03c2 \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 \u03c4\u03bf 0,313. \u0397 logistic regression \u03b5\u03af\u03c7\u03b5 accuracy 0,574 \u03ba\u03b1\u03b9 AUC 0,633, \u03b5\u03bd\u03ce \u03c4\u03bf MLP \u03b5\u03af\u03c7\u03b5 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b1 accuracy \u03ba\u03b1\u03b9 AUC, 0,606 \u03ba\u03b1\u03b9 0,657 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b1. \u039f \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc\u03c2 \u03bc\u03ad\u03c3\u03bf\u03c2 \u03ae\u03c4\u03b1\u03bd \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b1 \u03b4\u03cd\u03bf, \u03bc\u03b5 accuracy 0,511 \u03ba\u03b1\u03b9 AUC 0,498.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<div class=\"td-chart-head\">\n<p class=\"td-chart-title\">\u0397 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ae \u03b4\u03bf\u03ba\u03b9\u03bc\u03ae \u03c4\u03c9\u03bd aggregators<\/p>\n<p class=\"td-chart-subtitle\">\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2 arXiv:2607.18269v2 \u03c3\u03c4\u03b1 \u03af\u03b4\u03b9\u03b1 94 \u03b5\u03c1\u03c9\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03c0\u03bf\u03c5 \u03b5\u03c0\u03b9\u03bb\u03cd\u03b8\u03b7\u03ba\u03b1\u03bd \u03bc\u03b5\u03c4\u03ac \u03c4\u03b1 training cutoffs \u03cc\u03bb\u03c9\u03bd \u03c4\u03c9\u03bd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd. \u03a3\u03c4\u03bf Brier score, \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 \u03c4\u03b9\u03bc\u03ae \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03c0\u03b9\u03b8\u03b1\u03bd\u03bf\u03c4\u03b9\u03ba\u03ae \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7.<\/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\">94<\/span><span class=\"td-metric-label\">\u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ac \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac \u03b5\u03c1\u03c9\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1<\/span><span class=\"td-metric-note\">44 YES \u03ba\u03b1\u03b9 50 NO<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">0,241<\/span><span class=\"td-metric-label\">logistic regression<\/span><span class=\"td-metric-note\">\u03a4\u03bf \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf Brier score<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">0,264<\/span><span class=\"td-metric-label\">MLP aggregator<\/span><span class=\"td-metric-note\">\u039a\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf AUC \u03ba\u03b1\u03b9 accuracy \u03b1\u03c0\u03cc \u03c4\u03b7 logistic regression<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">0,313<\/span><span class=\"td-metric-label\">\u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc\u03c2 \u03bc\u03ad\u03c3\u03bf\u03c2<\/span><span class=\"td-metric-note\">\u03a4\u03bf \u03b1\u03c0\u03bb\u03cc baseline \u03c4\u03c9\u03bd 15 LLMs<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0397 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1 \u00ab\u03b2\u03b1\u03b8\u03cd\u00bb \u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03b1\u03bd\u03b1\u03ba\u03ac\u03bb\u03c5\u03c8\u03b5 \u03ba\u03ac\u03c0\u03bf\u03b9\u03b1 \u03bc\u03c5\u03c3\u03c4\u03b7\u03c1\u03b9\u03ce\u03b4\u03b7 \u03bc\u03b7 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03ae \u03bd\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7. \u0391\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b1, \u03c4\u03bf \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c4\u03b1\u03af\u03c1\u03b9\u03b1\u03be\u03b5 \u03ae \u03be\u03b5\u03c0\u03ad\u03c1\u03b1\u03c3\u03b5 \u03c4\u03bf MLP \u03c3\u03c4\u03bf Brier score. \u0391\u03c5\u03c4\u03cc \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03af\u03b1 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03ba\u03ad\u03c1\u03b4\u03bf\u03c2 \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03b5\u03c4\u03b1\u03b9 \u03ba\u03c5\u03c1\u03af\u03c9\u03c2 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b5\u03ba\u03bc\u03ac\u03b8\u03b7\u03c3\u03b7 \u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03c9\u03bd \u03b2\u03b1\u03c1\u03ce\u03bd \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac error patterns. \u0397 \u03c0\u03b9\u03b8\u03b1\u03bd\u03ae \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c4\u03b7 \u03bc\u03b7 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf\u03c5 MLP \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b5 \u03c4\u03b7 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd accuracy, \u03b1\u03bb\u03bb\u03ac \u03bc\u03b5 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c3\u03c4\u03bf calibration.<\/p>\n<p>\u0397 \u03c5\u03c0\u03b5\u03c1\u03bf\u03c7\u03ae \u03c4\u03bf\u03c5 MLP \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c4\u03bf\u03c5 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03bf\u03cd \u03bc\u03ad\u03c3\u03bf\u03c5 \u03ae\u03c4\u03b1\u03bd \u03c3\u03c5\u03bd\u03b5\u03c0\u03ae\u03c2 \u03c3\u03b5 62 \u03b1\u03c0\u03cc \u03c4\u03b1 94 items \u03ba\u03b1\u03b9 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03c7\u03b8\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc sign test, Wilcoxon test \u03ba\u03b1\u03b9 permutation test. \u0393\u03b9\u03b1 \u03c4\u03b7 logistic regression, \u03b7 \u03bc\u03ad\u03c3\u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03c4\u03bf\u03c5 Brier \u03ae\u03c4\u03b1\u03bd \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7, 23% \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 15,6% \u03c4\u03bf\u03c5 MLP, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03ba\u03b1\u03c4\u03b1\u03bd\u03ad\u03bc\u03bf\u03bd\u03c4\u03b1\u03bd \u03bf\u03bc\u03bf\u03b9\u03cc\u03bc\u03bf\u03c1\u03c6\u03b1 \u03c3\u03c4\u03b1 items. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c4\u03b7\u03bd \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03cd\u03bf\u03c5\u03bd \u03c9\u03c2 \u03bb\u03af\u03b3\u03b5\u03c2, \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b1 \u03b9\u03c3\u03c7\u03c5\u03c1\u03ad\u03c2 \u03bd\u03af\u03ba\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03c9\u03c2 \u03bc\u03b9\u03ba\u03c1\u03ae \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7 \u03c0\u03b1\u03bd\u03c4\u03bf\u03cd.<\/p>\n<h2 id=\"adynamo-montelo-axia-omada\">\u0393\u03b9\u03b1\u03c4\u03af \u03ad\u03bd\u03b1 \u00ab\u03b1\u03b4\u03cd\u03bd\u03b1\u03bc\u03bf\u00bb \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03bf\u03bb\u03cd\u03c4\u03b9\u03bc\u03bf \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03bf\u03bc\u03ac\u03b4\u03b1<\/h2>\n<p>\u0397 symbolic regression \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c8\u03b5\u03b9 \u03c4\u03b7 \u03bc\u03b1\u03b8\u03b7\u03bc\u03ad\u03bd\u03b7 \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03ac\u03c6\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 MLP \u03c3\u03b5 \u03c0\u03b9\u03bf \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03cd\u03c3\u03b9\u03bc\u03b7 \u03bc\u03bf\u03c1\u03c6\u03ae. \u03a3\u03b5 50 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b1 runs, \u03b7 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd \u03b4\u03b5\u03bd \u03b1\u03ba\u03bf\u03bb\u03bf\u03cd\u03b8\u03b7\u03c3\u03b5 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03c4\u03b7\u03bd \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03b1\u03c4\u03bf\u03bc\u03b9\u03ba\u03ae\u03c2 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7\u03c2. \u03a3\u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03c0\u03bf\u03c5 \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03bf\u03bd\u03c4\u03b1\u03bd \u03c3\u03c4\u03bf 100% \u03c4\u03c9\u03bd runs \u03c5\u03c0\u03ae\u03c1\u03c7\u03b1\u03bd \u03c4\u03cc\u03c3\u03bf \u03b9\u03c3\u03c7\u03c5\u03c1\u03ac cloud \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03cc\u03c3\u03bf \u03ba\u03b1\u03b9 \u03c4\u03c1\u03af\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b1 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b1 local \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1: Qwen 2.5 7B, LLaMA 3.1 8B \u03ba\u03b1\u03b9 Phi-4 14B.<\/p>\n<p>\u0397 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7\u03c2 \u03c0\u03bf\u03bb\u03c5\u03c0\u03bb\u03bf\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03c6\u03cc\u03c1\u03bc\u03bf\u03c5\u03bb\u03b1 \u03c3\u03c4\u03bf Pareto frontier \u03ae\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 contrastive signal \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03c4\u03b7\u03bd \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf\u03c5 Gemini Pro \u03ba\u03b1\u03b9 \u03c4\u03bf\u03c5 Claude Haiku. \u03a3\u03c4\u03b1 203 items \u03cc\u03c0\u03bf\u03c5 \u03b5\u03c0\u03ad\u03c3\u03c4\u03c1\u03b5\u03c8\u03b1\u03bd \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03b1 \u03b4\u03cd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, \u03b1\u03c5\u03c4\u03ae \u03b7 \u03c6\u03cc\u03c1\u03bc\u03bf\u03c5\u03bb\u03b1 \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 Brier score 0,231, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae 12,9% \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03bf 0,265 \u03c4\u03bf\u03c5 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03bf\u03cd \u03bc\u03ad\u03c3\u03bf\u03c5 \u03c3\u03c4\u03b1 \u03af\u03b4\u03b9\u03b1 items. \u03a3\u03c4\u03bf clean subset \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 0,243. \u0394\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b1\u03c5\u03c4\u03ac \u03c4\u03b1 \u03b4\u03cd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ae \u03c3\u03c5\u03bd\u03c4\u03b1\u03b3\u03ae. \u03a3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9, \u03c3\u03b5 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf dataset, \u03bc\u03b9\u03b1 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03c9\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03c3\u03c6\u03b1\u03bb\u03bc\u03ac\u03c4\u03c9\u03bd \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b5 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03ce\u03c3\u03b5\u03b9 \u03c3\u03c7\u03b5\u03b4\u03cc\u03bd \u03cc\u03c3\u03bf \u03ad\u03bd\u03b1 \u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03c0\u03bf\u03c5 \u03ad\u03b2\u03bb\u03b5\u03c0\u03b5 \u03ba\u03b1\u03b9 \u03c4\u03b1 15 outputs.<\/p>\n<p>\u0397 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03c4\u03c9\u03bd coefficients \u03c4\u03b7\u03c2 logistic regression \u03b5\u03bd\u03af\u03c3\u03c7\u03c5\u03c3\u03b5 \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7\u03bd \u03b5\u03be\u03ae\u03b3\u03b7\u03c3\u03b7. \u0397 \u03b1\u03c0\u03cc\u03bb\u03c5\u03c4\u03b7 \u03b2\u03b1\u03c1\u03cd\u03c4\u03b7\u03c4\u03b1 \u03b5\u03bd\u03cc\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03c3\u03c5\u03c3\u03c7\u03b5\u03c4\u03b9\u03b6\u03cc\u03c4\u03b1\u03bd \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03bc\u03b5 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf\u03c5 \u03c3\u03c6\u03ac\u03bb\u03bc\u03b1\u03c4\u03cc\u03c2 \u03c4\u03bf\u03c5, Spearman <em>r<\/em><sub>s<\/sub> = 0,482, \u03ba\u03b1\u03b9 \u03c3\u03c7\u03b5\u03b4\u03cc\u03bd \u03ba\u03b1\u03b8\u03cc\u03bb\u03bf\u03c5 \u03bc\u03b5 \u03c4\u03b7\u03bd \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03c4\u03b7\u03c2 \u03b1\u03c4\u03bf\u03bc\u03b9\u03ba\u03ae\u03c2 \u03c4\u03bf\u03c5 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7\u03c2, <em>r<\/em><sub>s<\/sub> = \u22120,075. \u0395\u03bd\u03bd\u03ad\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b1 15 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ad\u03bb\u03b1\u03b2\u03b1\u03bd \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03ac weights. \u0394\u03b7\u03bb\u03b1\u03b4\u03ae, \u03cc\u03c4\u03b1\u03bd \u03ba\u03ac\u03c0\u03bf\u03b9\u03bf \u03b1\u03c0\u03cc \u03b1\u03c5\u03c4\u03ac \u03b1\u03bd\u03ad\u03b2\u03b1\u03b6\u03b5 \u03c4\u03b7\u03bd \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1, \u03bf aggregator \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b5 \u03bd\u03b1 \u03c4\u03b7 \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9. \u0388\u03bd\u03b1 \u03c3\u03c5\u03c3\u03c4\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc bias \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03cc\u03c4\u03b1\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03b1\u03c0\u03cc \u03c4\u03b1 biases \u03c4\u03b7\u03c2 \u03c5\u03c0\u03cc\u03bb\u03bf\u03b9\u03c0\u03b7\u03c2 \u03bf\u03bc\u03ac\u03b4\u03b1\u03c2.<\/p>\n<h2 id=\"contamination-katataxi-montelon\">\u03a4\u03bf contamination \u03ac\u03bb\u03bb\u03b1\u03be\u03b5 \u03b4\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03c4\u03b7\u03bd \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03c4\u03c9\u03bd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd<\/h2>\n<p>\u0397 \u03c0\u03b9\u03bf \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7 \u03c0\u03c1\u03bf\u03b5\u03b9\u03b4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b1\u03c6\u03bf\u03c1\u03ac \u03c4\u03b1 training cutoffs. \u0391\u03bd \u03b7 \u03ad\u03ba\u03b2\u03b1\u03c3\u03b7 \u03b5\u03bd\u03cc\u03c2 \u03b5\u03c1\u03c9\u03c4\u03ae\u03bc\u03b1\u03c4\u03bf\u03c2 \u03b5\u03af\u03c7\u03b5 \u03b3\u03af\u03bd\u03b5\u03b9 \u03b4\u03b7\u03bc\u03cc\u03c3\u03b9\u03b1 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf cutoff \u03b5\u03bd\u03cc\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5, \u03b7 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b2\u03c1\u03b9\u03c3\u03ba\u03cc\u03c4\u03b1\u03bd \u03c3\u03c4\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7\u03c2. \u03a4\u03cc\u03c4\u03b5 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b4\u03b5\u03bd \u03ba\u03ac\u03bd\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7. \u0395\u03bd\u03b4\u03ad\u03c7\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03bd\u03b1\u03ba\u03b1\u03bb\u03b5\u03af, \u03bc\u03b5 \u03ac\u03bc\u03b5\u03c3\u03bf \u03ae \u03ad\u03bc\u03bc\u03b5\u03c3\u03bf \u03c4\u03c1\u03cc\u03c0\u03bf, \u03ad\u03bd\u03b1 \u03ae\u03b4\u03b7 \u03b3\u03bd\u03c9\u03c3\u03c4\u03cc \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1.<\/p>\n<p>\u038c\u03bb\u03b1 \u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03bc\u03b5 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac within-cutoff items \u03b5\u03bc\u03c6\u03ac\u03bd\u03b9\u03c3\u03b1\u03bd \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 accuracy \u03c3\u03b5 \u03b1\u03c5\u03c4\u03ac \u03b1\u03c0\u03cc \u03cc,\u03c4\u03b9 \u03c3\u03c4\u03b1 out-of-window items. \u0397 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 \u03b1\u03c0\u03cc +0,05 \u03b3\u03b9\u03b1 \u03c4\u03bf GPT-5.4 Mini \u03ad\u03c9\u03c2 +0,26 \u03b3\u03b9\u03b1 \u03c4\u03bf Claude Sonnet. \u03a4\u03b1 Gemini Flash \u03ba\u03b1\u03b9 Gemini Pro \u03b5\u03bc\u03c6\u03ac\u03bd\u03b9\u03c3\u03b1\u03bd \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03c3\u03c4\u03b1\u03c4\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ac \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 \u00ab\u03b1\u03ba\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1\u00bb \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03c3\u03c4\u03b1 within-cutoff items, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03c0\u03cc\u03c3\u03c4\u03b1\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03bf 0,5. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c0\u03b1\u03c1\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b1\u03bd \u03cc\u03c4\u03b9 \u03c4\u03b1 Claude \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b1\u03bd \u03bd\u03b1 \u03ad\u03c7\u03bf\u03c5\u03bd \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf \u03c0\u03bb\u03b5\u03bf\u03bd\u03ad\u03ba\u03c4\u03b7\u03bc\u03b1 accuracy \u03c7\u03c9\u03c1\u03af\u03c2 \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03b1\u03cd\u03be\u03b7\u03c3\u03b7 \u03b1\u03ba\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2, \u03ba\u03ac\u03c4\u03b9 \u03c0\u03bf\u03c5 \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03cd\u03bf\u03c5\u03bd \u03c0\u03c1\u03bf\u03c3\u03b5\u03ba\u03c4\u03b9\u03ba\u03ac \u03c9\u03c2 \u03c0\u03b9\u03b8\u03b1\u03bd\u03ae \u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7 calibration training.<\/p>\n<p>\u0397 \u03c3\u03c5\u03c3\u03c7\u03ad\u03c4\u03b9\u03c3\u03b7 Spearman \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03c4\u03b7\u03bd \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03c4\u03c9\u03bd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd \u03c3\u03c4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 dataset \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf clean subset \u03ae\u03c4\u03b1\u03bd \u03bc\u03cc\u03bb\u03b9\u03c2 0,532. \u03a4\u03bf LLaMA 3.1 8B \u03b1\u03bd\u03ad\u03b2\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc \u03c4\u03b7 14\u03b7 \u03c3\u03c4\u03b7\u03bd 6\u03b7 \u03b8\u03ad\u03c3\u03b7, \u03c4\u03bf GPT-5.4 Mini \u03ad\u03c0\u03b5\u03c3\u03b5 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd 6\u03b7 \u03c3\u03c4\u03b7 13\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03bf Claude Opus \u03b1\u03c0\u03cc \u03c4\u03b7 2\u03b7 \u03c3\u03c4\u03b7\u03bd 9\u03b7. \u0391\u03ba\u03cc\u03bc\u03b7 \u03c0\u03b9\u03bf \u03b5\u03bd\u03c4\u03c5\u03c0\u03c9\u03c3\u03b9\u03b1\u03ba\u03ac, \u03c4\u03bf \u03c7\u03ac\u03c3\u03bc\u03b1 cloud \u03ba\u03b1\u03b9 local \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd \u03bc\u03b5\u03b9\u03ce\u03b8\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc 35,8% \u03c3\u03c4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03c3\u03b5 8,9% \u03c3\u03c4\u03b1 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac items \u03bc\u03b5 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc \u03bc\u03ad\u03c3\u03bf.<\/p>\n<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03ad\u03ba\u03b1\u03bd\u03b5 \u03b4\u03cd\u03bf robustness checks. \u038c\u03c4\u03b1\u03bd \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 \u03bc\u03cc\u03bd\u03bf \u03c4\u03b1 48 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac items \u03c0\u03bf\u03c5 \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03bf\u03bd\u03c4\u03b1\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf \u03b1\u03c1\u03c7\u03b9\u03ba\u03cc dataset, \u03ce\u03c3\u03c4\u03b5 \u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03c3\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae\u03c2 \u03c3\u03c4\u03b7 \u03b4\u03c5\u03c3\u03ba\u03bf\u03bb\u03af\u03b1 \u03ae \u03c3\u03c4\u03bf \u03b5\u03af\u03b4\u03bf\u03c2 \u03c4\u03c9\u03bd \u03b5\u03c1\u03c9\u03c4\u03ae\u03c3\u03b5\u03c9\u03bd, \u03c4\u03bf \u03c7\u03ac\u03c3\u03bc\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03c3\u03c4\u03b7\u03ba\u03b5 \u03c3\u03c4\u03bf 13,4%. \u038c\u03c4\u03b1\u03bd \u03bc\u03b5\u03c4\u03b1\u03ba\u03af\u03bd\u03b7\u03c3\u03b5 \u03cc\u03bb\u03b1 \u03c4\u03b1 \u03ba\u03b1\u03c4\u03ac \u03c0\u03c1\u03bf\u03c3\u03ad\u03b3\u03b3\u03b9\u03c3\u03b7 cutoffs \u03ba\u03b1\u03c4\u03ac \u03ad\u03bd\u03b1\u03bd \u03bc\u03ae\u03bd\u03b1 \u03c0\u03c1\u03bf\u03c2 \u03ba\u03ac\u03b8\u03b5 \u03ba\u03b1\u03c4\u03b5\u03cd\u03b8\u03c5\u03bd\u03c3\u03b7, \u03c4\u03b1 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ac \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c0\u03b1\u03c1\u03ad\u03bc\u03b5\u03b9\u03bd\u03b1\u03bd \u03c0\u03b1\u03c1\u03cc\u03bc\u03bf\u03b9\u03b1: \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 36% \u03c7\u03ac\u03c3\u03bc\u03b1 \u03c3\u03c4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf, \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 13% \u03c3\u03c4\u03b1 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac items \u03ba\u03b1\u03b9 correlations \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7\u03c2 \u03ba\u03bf\u03bd\u03c4\u03ac \u03c3\u03c4\u03bf 0,5.<\/p>\n<h2 id=\"llm-crowds-anthropini-agora\">\u03a4\u03b1 LLM crowds \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03c6\u03c4\u03ac\u03bd\u03bf\u03c5\u03bd \u03c4\u03b7 \u03b4\u03c5\u03bd\u03b1\u03bc\u03b9\u03ba\u03ae \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03b1\u03b3\u03bf\u03c1\u03ac<\/h2>\n<p>\u0397 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03b7\u03c2 \u03b1\u03b3\u03bf\u03c1\u03ac\u03c2 Manifold \u03b5\u03af\u03c7\u03b5 Brier score 0,098 \u03c3\u03c4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03c4\u03c9\u03bd 208 items, \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 0,266 \u03b3\u03b9\u03b1 \u03c4\u03bf\u03bd \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc \u03bc\u03ad\u03c3\u03bf \u03c4\u03c9\u03bd LLMs. \u0397 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b1\u03c5\u03c4\u03ae \u03b5\u03c5\u03bd\u03bf\u03b5\u03af \u03c6\u03c5\u03c3\u03b9\u03ba\u03ac \u03c4\u03b7\u03bd \u03b1\u03b3\u03bf\u03c1\u03ac, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03bf\u03b9 traders \u03b5\u03bd\u03b7\u03bc\u03ad\u03c1\u03c9\u03bd\u03b1\u03bd \u03c4\u03b9\u03c2 \u03b5\u03ba\u03c4\u03b9\u03bc\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03ad\u03c9\u03c2 \u03c4\u03b7\u03bd \u03b5\u03c0\u03af\u03bb\u03c5\u03c3\u03b7, \u03b5\u03bd\u03ce \u03c4\u03b1 LLMs \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03bf\u03cd\u03c3\u03b1\u03bd \u03bc\u03b5 \u03c0\u03b1\u03b3\u03c9\u03bc\u03ad\u03bd\u03b7 \u03b3\u03bd\u03ce\u03c3\u03b7 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7\u03c2.<\/p>\n<p>\u0393\u03b9\u03b1 \u03c0\u03b9\u03bf \u03b4\u03af\u03ba\u03b1\u03b9\u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7, \u03bf\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03ad\u03c2 \u03ac\u03bd\u03c4\u03bb\u03b7\u03c3\u03b1\u03bd \u03c4\u03b7\u03bd \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03ae \u03c4\u03b9\u03bc\u03ae \u03ba\u03ac\u03b8\u03b5 \u03b1\u03b3\u03bf\u03c1\u03ac\u03c2 \u03c3\u03c4\u03b7 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ae \u03c3\u03c4\u03b9\u03b3\u03bc\u03ae \u03c4\u03bf\u03c5 cutoff \u03ba\u03ac\u03b8\u03b5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5. \u0391\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03cc\u03c4\u03b5, \u03ba\u03ac\u03b8\u03b5 \u03bc\u03b5\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03bf LLM \u03b5\u03af\u03c7\u03b5 Brier score 1,61 \u03ad\u03c9\u03c2 2,64 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c7\u03b5\u03b9\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b7 \u03c4\u03b9\u03bc\u03ae \u03c4\u03b7\u03c2 \u03b1\u03b3\u03bf\u03c1\u03ac\u03c2, \u03bc\u03b5 \u03bc\u03ad\u03c3\u03bf \u03bb\u03cc\u03b3\u03bf 1,95. \u039f \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc\u03c2 \u03bc\u03ad\u03c3\u03bf\u03c2 \u03c4\u03c9\u03bd LLMs \u03ae\u03c4\u03b1\u03bd 2,01 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c7\u03b5\u03b9\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf\u03c2 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03b3\u03bf\u03c1\u03ac \u03c3\u03c4\u03bf \u03c0\u03b9\u03bf \u03c0\u03c1\u03cc\u03c3\u03c6\u03b1\u03c4\u03bf \u03ba\u03bf\u03b9\u03bd\u03cc cutoff, \u03c4\u03bf\u03bd \u0391\u03cd\u03b3\u03bf\u03c5\u03c3\u03c4\u03bf \u03c4\u03bf\u03c5 2025.<\/p>\n<p>\u0397 \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03af\u03b1 \u03c4\u03b7\u03c2 \u03c0\u03b7\u03b3\u03ae\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03b1\u03b3\u03bf\u03c1\u03ac \u03b4\u03b5\u03bd \u03c5\u03c0\u03b5\u03c1\u03ad\u03c7\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03ad\u03c7\u03b5\u03b9 \u03bd\u03b5\u03cc\u03c4\u03b5\u03c1\u03b7 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1. \u0395\u03bd\u03c3\u03c9\u03bc\u03b1\u03c4\u03ce\u03bd\u03b5\u03b9 \u03b4\u03b9\u03b1\u03c3\u03ba\u03bf\u03c1\u03c0\u03b9\u03c3\u03bc\u03ad\u03bd\u03b7 \u03b3\u03bd\u03ce\u03c3\u03b7 \u03b1\u03c0\u03cc traders \u03c0\u03bf\u03c5 \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03bf\u03c5\u03bd \u03c4\u03b1 \u03c0\u03b5\u03b4\u03af\u03b1 \u03c4\u03bf\u03c5\u03c2, \u03c3\u03c4\u03bf\u03b9\u03c7\u03b7\u03bc\u03b1\u03c4\u03af\u03b6\u03bf\u03c5\u03bd \u03b5\u03c0\u03b1\u03bd\u03b5\u03b9\u03bb\u03b7\u03bc\u03bc\u03ad\u03bd\u03b1, \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03bf\u03cd\u03bd \u03c4\u03b9\u03c2 \u03b5\u03ba\u03c4\u03b9\u03bc\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c4\u03c9\u03bd \u03ac\u03bb\u03bb\u03c9\u03bd \u03ba\u03b1\u03b9 \u03b1\u03bd\u03c4\u03b9\u03b4\u03c1\u03bf\u03cd\u03bd \u03c3\u03b5 \u03bd\u03ad\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1. \u03a4\u03b1 LLMs \u03c4\u03b7\u03c2 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7\u03c2 \u03ad\u03b4\u03c9\u03c3\u03b1\u03bd \u03c4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03b5\u03c2, \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b5\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c7\u03c9\u03c1\u03af\u03c2 \u03b4\u03b9\u03ac\u03bb\u03bf\u03b3\u03bf \u03ba\u03b1\u03b9 \u03c7\u03c9\u03c1\u03af\u03c2 \u03b5\u03bd\u03b7\u03bc\u03ad\u03c1\u03c9\u03c3\u03b7. \u03a0\u03c1\u03cc\u03ba\u03b5\u03b9\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc \u03c3\u03c5\u03bb\u03bb\u03bf\u03b3\u03b9\u03ba\u03ae\u03c2 \u03bd\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2.<\/p>\n<h2 id=\"epicheiriseis-marketers-ai-workflows\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2, marketers \u03ba\u03b1\u03b9 AI workflows<\/h2>\n<p>\u03a4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf \u00ab\u03c1\u03ce\u03c4\u03b7\u03c3\u03b1 \u03c4\u03c1\u03af\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03b1 \u03b4\u03cd\u03bf \u03c3\u03c5\u03bc\u03c6\u03ce\u03bd\u03b7\u03c3\u03b1\u03bd\u00bb \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af \u03b5\u03c0\u03b1\u03c1\u03ba\u03ae ensemble strategy. \u0397 \u03c0\u03bb\u03b5\u03b9\u03bf\u03c8\u03b7\u03c6\u03af\u03b1 \u03b1\u03b3\u03bd\u03bf\u03b5\u03af \u03c4\u03bf calibration, \u03c4\u03b7 \u03c3\u03c5\u03c3\u03c7\u03ad\u03c4\u03b9\u03c3\u03b7 \u03c4\u03c9\u03bd \u03bb\u03b1\u03b8\u03ce\u03bd \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ae \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03ba\u03ac\u03b8\u03b5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5. \u0391\u03bd \u03ad\u03bd\u03b1 workflow \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c0\u03bf\u03bb\u03bb\u03ac LLMs, \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc dataset \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03ba\u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03c5\u03c3\u03c4\u03b7\u03c1\u03cc \u03b4\u03b9\u03b1\u03c7\u03c9\u03c1\u03b9\u03c3\u03bc\u03cc \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 training \u03ba\u03b1\u03b9 evaluation periods.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03bf\u03c5 \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03b6\u03b5\u03b9 <a href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u0391\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u0395\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03c9\u03bd &amp; AI<\/a>, \u03c4\u03bf ensemble \u03ad\u03c7\u03b5\u03b9 \u03b1\u03be\u03af\u03b1 \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b1\u03bd \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7, \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03bb\u03ac\u03b8\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7\u03c2 \u03ad\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7\u03c2. \u0397 <a href=\"https:\/\/twodots.gr\/craft-apo-ta-scores-sti-stochevmeni-veltiosi-ton-montelon-ai\/\">\u03c3\u03c4\u03bf\u03c7\u03b5\u03c5\u03bc\u03ad\u03bd\u03b7 \u03b4\u03b9\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7 \u03b1\u03b4\u03c5\u03bd\u03b1\u03bc\u03b9\u03ce\u03bd \u03c4\u03c9\u03bd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd<\/a> \u03b2\u03bf\u03b7\u03b8\u03ac \u03bd\u03b1 \u03c6\u03b1\u03bd\u03b5\u03af \u03b1\u03bd \u03b7 \u03b4\u03b9\u03b1\u03c6\u03c9\u03bd\u03af\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03ae \u03b1\u03c0\u03bb\u03ce\u03c2 \u03b8\u03cc\u03c1\u03c5\u03b2\u03bf, \u03b5\u03bd\u03ce \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/llm-routing-latency-accuracy-cost\/\">LLM routing \u03bc\u03b5 \u03ba\u03c1\u03b9\u03c4\u03ae\u03c1\u03b9\u03b1 latency, accuracy \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2<\/a> \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03c0\u03bf\u03b9\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03ba\u03c4\u03b5\u03bb\u03ad\u03c3\u03b5\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03b1\u03af\u03c4\u03b7\u03bc\u03b1. \u039a\u03b1\u03b9 \u03c3\u03c4\u03b9\u03c2 \u03b4\u03cd\u03bf \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2, \u03c4\u03b1 <a href=\"https:\/\/twodots.gr\/ai-agents-confidence-context-data-human-oversight\/\">\u03cc\u03c1\u03b9\u03b1 confidence, context \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7\u03c2 \u03b5\u03c0\u03bf\u03c0\u03c4\u03b5\u03af\u03b1\u03c2<\/a> \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03bf\u03c1\u03b1\u03c4\u03ac.<\/p>\n<p>\u03a4\u03bf \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd \u03bc\u03cc\u03bd\u03bf \u03b1\u03c0\u03cc leaderboards \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b5\u03b9\u03ce\u03c3\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03be\u03af\u03b1 \u03c4\u03bf\u03c5 ensemble. \u0394\u03ad\u03ba\u03b1 \u03c0\u03bf\u03bb\u03cd \u03c0\u03b1\u03c1\u03cc\u03bc\u03bf\u03b9\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b3\u03bf\u03c5\u03bd \u03b4\u03ad\u03ba\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 \u03c4\u03bf\u03c5 \u03af\u03b4\u03b9\u03bf\u03c5 \u03bb\u03ac\u03b8\u03bf\u03c5\u03c2. \u0388\u03bd\u03b1 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03ae \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03ba\u03c1\u03b9\u03b2\u03ad\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03c6\u03ad\u03c1\u03b5\u03b9 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03bf\u03c1\u03b9\u03b1\u03ba\u03ae \u03b1\u03be\u03af\u03b1 \u03b1\u03bd \u03c4\u03bf error pattern \u03c4\u03bf\u03c5 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03bf. \u0397 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b5\u03c1\u03ce\u03c4\u03b7\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u00ab\u03c0\u03bf\u03b9\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ad\u03c7\u03b5\u03b9 \u03c4\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf score;\u00bb, \u03b1\u03bb\u03bb\u03ac \u00ab\u03c0\u03bf\u03b9\u03bf \u03bd\u03ad\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03ae\u03b4\u03b7;\u00bb.<\/p>\n<p>\u03a4\u03bf \u03c4\u03c1\u03af\u03c4\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03b1 \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03ac weights \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b8\u03b5\u03c9\u03c1\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03c0\u03b1\u03c1\u03ac\u03bb\u03bf\u03b3\u03b1. \u03a3\u03b5 \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 risk scoring, demand forecasting \u03ae \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2 \u03c0\u03b5\u03c1\u03b9\u03b5\u03c7\u03bf\u03bc\u03ad\u03bd\u03bf\u03c5, \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03c9\u03c2 \u03b1\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03bf\u03c2 \u03b4\u03b5\u03af\u03ba\u03c4\u03b7\u03c2 \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf bias \u03c4\u03bf\u03c5 \u03ad\u03c7\u03b5\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03c7\u03b8\u03b5\u03af \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc. \u0391\u03c5\u03c4\u03cc \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03c0\u03c1\u03bf\u03c3\u03b5\u03ba\u03c4\u03b9\u03ba\u03cc validation \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03c7\u03b5\u03b9\u03c1\u03bf\u03ba\u03af\u03bd\u03b7\u03c4\u03b7 \u03c5\u03c0\u03cc\u03b8\u03b5\u03c3\u03b7. \u03a4\u03b1 weights \u03c4\u03b7\u03c2 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7\u03c2 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7\u03c2 \u03b4\u03b5\u03bd \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c5\u03c4\u03bf\u03bc\u03ac\u03c4\u03c9\u03c2 \u03c3\u03b5 \u03ac\u03bb\u03bb\u03bf domain, \u03ac\u03bb\u03bb\u03bf prompt \u03ae \u03bd\u03b5\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd.<\/p>\n<p>\u0393\u03b9\u03b1 \u03ad\u03bd\u03b1\u03bd e-commerce \u03bf\u03c1\u03b3\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc, \u03b7 \u03b9\u03b4\u03ad\u03b1 \u03b8\u03b1 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b5 \u03bd\u03b1 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03c3\u03c4\u03b5\u03af \u03c3\u03b5 \u03ba\u03b1\u03bb\u03ac \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b4\u03c5\u03b1\u03b4\u03b9\u03ba\u03ad\u03c2 \u03ae \u03c0\u03b9\u03b8\u03b1\u03bd\u03bf\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2, \u03cc\u03c0\u03c9\u03c2 \u03b1\u03bd \u03ad\u03bd\u03b1 SKU \u03ba\u03b9\u03bd\u03b4\u03c5\u03bd\u03b5\u03cd\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03be\u03b1\u03bd\u03c4\u03bb\u03b7\u03b8\u03b5\u03af \u03c3\u03b5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03bf\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b1 \u03ae \u03b1\u03bd \u03bc\u03b9\u03b1 \u03ba\u03b1\u03bc\u03c0\u03ac\u03bd\u03b9\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc \u03bd\u03b1 \u03c0\u03b5\u03c4\u03cd\u03c7\u03b5\u03b9 \u03c0\u03c1\u03bf\u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf \u03c3\u03c4\u03cc\u03c7\u03bf. \u038c\u03bc\u03c9\u03c2 \u03b7 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03b5\u03c3\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ac, \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ac \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, \u03c0\u03c1\u03bf\u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf metric \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03b5\u03c7\u03ae \u03b5\u03c0\u03b1\u03bd\u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7. \u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1 \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc LLM ensemble \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03c4\u03b1\u03c3\u03c4\u03ae\u03c3\u03b5\u03b9 \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03c5\u03bc\u03ad\u03bd\u03bf forecasting \u03ae causal analysis.<\/p>\n<h2 id=\"asfalestero-plaisio-multi-model-apofaseis\">\u0388\u03bd\u03b1 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c3\u03c4\u03b5\u03c1\u03bf \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03b3\u03b9\u03b1 multi-model \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2<\/h2>\n<p>\u0388\u03bd\u03b1 \u03bf\u03c1\u03b3\u03b1\u03bd\u03c9\u03bc\u03ad\u03bd\u03bf pilot \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03be\u03b5\u03ba\u03b9\u03bd\u03ae\u03c3\u03b5\u03b9 \u03bc\u03b5 \u03bc\u03af\u03b1 \u03c3\u03b1\u03c6\u03ce\u03c2 \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b7 \u03ad\u03ba\u03b2\u03b1\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03b1\u03bd\u03c4\u03af \u03b3\u03b9\u03b1 \u03b5\u03bb\u03b5\u03cd\u03b8\u03b5\u03c1\u03bf \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf. \u039a\u03ac\u03b8\u03b5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf prompt, \u03c4\u03bf \u03af\u03b4\u03b9\u03bf information cutoff \u03ba\u03b1\u03b9 \u03c4\u03bf\u03c5\u03c2 \u03af\u03b4\u03b9\u03bf\u03c5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2. \u03a4\u03b1 outputs \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b5\u03c1\u03bd\u03bf\u03cd\u03bd \u03b1\u03c0\u03cc validation, \u03b5\u03bd\u03ce failures \u03ba\u03b1\u03b9 refusals \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03bd\u03c4\u03af \u03bd\u03b1 \u03b5\u03be\u03b1\u03c6\u03b1\u03bd\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf dataset.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u0388\u03be\u03b9 \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf multi-model pilot<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 1<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03bc\u03af\u03b1 \u03b5\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03cd\u03c3\u03b9\u03bc\u03b7 \u03ad\u03ba\u03b2\u03b1\u03c3\u03b7<\/strong>\n<p>\u0395\u03c0\u03b9\u03bb\u03ad\u03be\u03c4\u03b5 \u03b4\u03c5\u03b1\u03b4\u03b9\u03ba\u03cc \u03ae \u03c0\u03b9\u03b8\u03b1\u03bd\u03bf\u03c4\u03b9\u03ba\u03cc \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03bc\u03b5 \u03c3\u03b1\u03c6\u03ae \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03cc \u03bf\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b1, \u03cc\u03c0\u03c9\u03c2 stockout \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5 SKU \u03ae \u03b5\u03c0\u03af\u03c4\u03b5\u03c5\u03be\u03b7 \u03c0\u03c1\u03bf\u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf\u03c5 campaign \u03c3\u03c4\u03cc\u03c7\u03bf\u03c5.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 2<\/span><strong>\u039a\u03bb\u03b5\u03b9\u03b4\u03ce\u03c3\u03c4\u03b5 \u03ba\u03bf\u03b9\u03bd\u03cc information cutoff<\/strong>\n<p>\u0394\u03ce\u03c3\u03c4\u03b5 \u03c3\u03b5 \u03cc\u03bb\u03b1 \u03c4\u03b1 LLMs \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b7 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03ba\u03b1\u03b9 \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 prompt, model identifier, \u03b7\u03bc\u03b5\u03c1\u03bf\u03bc\u03b7\u03bd\u03af\u03b1 \u03ba\u03bb\u03ae\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03b3\u03bd\u03c9\u03c3\u03c4\u03cc training cutoff.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 3<\/span><strong>\u0396\u03b7\u03c4\u03ae\u03c3\u03c4\u03b5 \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b3\u03bd\u03ce\u03bc\u03b7<\/strong>\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03ba\u03bf\u03b9\u03bd\u03ae \u03ad\u03be\u03bf\u03b4\u03bf \u03b1\u03c0\u03cc 0 \u03ad\u03c9\u03c2 1, validation \u03c4\u03bf\u03c5 format \u03ba\u03b1\u03b9 \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03b1\u03c6\u03ae refusals \u03ae \u03b1\u03c0\u03bf\u03c4\u03c5\u03c7\u03b9\u03ce\u03bd \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b5\u03be\u03b1\u03c6\u03b1\u03bd\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf dataset.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 4<\/span><strong>\u03a7\u03c9\u03c1\u03af\u03c3\u03c4\u03b5 \u03c4\u03b1 cases \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ac<\/strong>\n<p>\u0395\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03c3\u03c4\u03b5 weights \u03bc\u03cc\u03bd\u03bf \u03c3\u03b5 \u03c0\u03b1\u03bb\u03b1\u03b9\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b5\u03c0\u03b9\u03bb\u03c5\u03bc\u03ad\u03bd\u03b5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03c4\u03b5 \u03c4\u03b1 \u03c3\u03b5 \u03bd\u03b5\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b5\u03ba\u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b1\u03bd \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b3\u03bd\u03c9\u03c3\u03c4\u03ad\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 5<\/span><strong>\u03a3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 \u03bc\u03b5 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac baselines<\/strong>\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c4\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03b5\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, \u03c4\u03bf\u03bd \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc \u03bc\u03ad\u03c3\u03bf \u03ba\u03b1\u03b9 \u03bc\u03b9\u03b1 \u03c5\u03c0\u03ac\u03c1\u03c7\u03bf\u03c5\u03c3\u03b1 \u03bc\u03b7-AI \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03bc\u03b5 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf metric.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0392\u03ae\u03bc\u03b1 6<\/span><strong>\u03a0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03ae\u03c3\u03c4\u03b5 drift \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03c3\u03c4\u03b5<\/strong>\n<p>\u039a\u03ac\u03b8\u03b5 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7\u03c2, prompt, \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03c9\u03bd \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03ae business \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1\u03c2 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03bd\u03ad\u03bf \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03c4\u03c9\u03bd weights, \u03c4\u03bf\u03c5 calibration \u03ba\u03b1\u03b9 \u03c4\u03b7\u03c2 \u03bf\u03c1\u03b9\u03b1\u03ba\u03ae\u03c2 \u03b1\u03be\u03af\u03b1\u03c2 \u03ba\u03ac\u03b8\u03b5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<p>\u03a3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1, \u03c4\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03bf\u03cd\u03bd \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ac. \u039f\u03b9 weights \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c3\u03b5 \u03c0\u03b1\u03bb\u03b1\u03b9\u03cc\u03c4\u03b5\u03c1\u03b1 resolved cases \u03ba\u03b1\u03b9 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03bc\u03b5\u03c4\u03b1\u03b3\u03b5\u03bd\u03ad\u03c3\u03c4\u03b5\u03c1\u03b1 cases \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b1\u03bd \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b3\u03bd\u03c9\u03c3\u03c4\u03ac. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03bc\u03b5 \u03b1\u03c0\u03bb\u03ac baselines: \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03b5\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc \u03bc\u03ad\u03c3\u03bf \u03ba\u03b1\u03b9 \u03bc\u03b9\u03b1 \u03bc\u03b7-AI \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7. \u0391\u03bd \u03bf learned aggregator \u03b4\u03b5\u03bd \u03bd\u03b9\u03ba\u03ac \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac \u03b1\u03c5\u03c4\u03ac \u03c4\u03b1 baselines, \u03b7 \u03c0\u03bf\u03bb\u03c5\u03c0\u03bb\u03bf\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b4\u03b5\u03bd \u03b4\u03b9\u03ba\u03b1\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af\u03c4\u03b1\u03b9.<\/p>\n<p>\u03a4\u03ad\u03bb\u03bf\u03c2, \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 drift \u03ba\u03b1\u03b9 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03c9\u03bd. \u03a4\u03b1 model identifiers, \u03c4\u03b1 prompts, \u03c4\u03b1 cutoffs \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03b7\u03bc\u03b5\u03c1\u03bf\u03bc\u03b7\u03bd\u03af\u03b5\u03c2 \u03ba\u03bb\u03ae\u03c3\u03b5\u03c9\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 audit trail. \u0388\u03bd\u03b1\u03c2 aggregator \u03c0\u03bf\u03c5 \u03ad\u03bc\u03b1\u03b8\u03b5 \u03c4\u03b7 \u03c3\u03c7\u03ad\u03c3\u03b7 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c4\u03cd\u03c7\u03b5\u03b9 \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1\u03c2 \u03c0\u03ac\u03c1\u03bf\u03c7\u03bf\u03c2 \u03b5\u03bd\u03b7\u03bc\u03b5\u03c1\u03ce\u03c3\u03b5\u03b9 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c4\u03bf\u03c5. \u0397 \u00ab\u03c3\u03bf\u03c6\u03af\u03b1\u00bb \u03c4\u03bf\u03c5 ensemble \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03cc\u03bd\u03b9\u03bc\u03b7 \u03b9\u03b4\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u00b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03bc\u03c0\u03b5\u03b9\u03c1\u03b9\u03ba\u03cc \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03ad\u03bb\u03b5\u03b3\u03c7\u03bf.<\/p>\n<h2 id=\"periorismoi-genikefseon\">\u039f\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03bf\u03c5\u03bd \u03b5\u03cd\u03ba\u03bf\u03bb\u03b5\u03c2 \u03b3\u03b5\u03bd\u03b9\u03ba\u03b5\u03cd\u03c3\u03b5\u03b9\u03c2<\/h2>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c3\u03b7\u03bc\u03b5\u03b9\u03ce\u03bd\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03c4\u03b1 94 clean items \u03b5\u03af\u03bd\u03b1\u03b9 \u03bb\u03af\u03b3\u03b1 \u03b3\u03b9\u03b1 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ae \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03bc\u03b5\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03c9\u03bd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd \u03ae \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03b5\u03c1\u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03c5\u03c0\u03bf\u03bf\u03bc\u03ac\u03b4\u03c9\u03bd. \u03a0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03bf\u03c5\u03bd \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b5\u03c2 \u03bc\u03b5 \u03c4\u03bf\u03c5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03bf\u03bd 200 \u03ad\u03c9\u03c2 300 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac items, \u03ce\u03c3\u03c4\u03b5 \u03bd\u03b1 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03ba\u03b1\u03b9 \u03bf\u03b9 learned aggregators \u03bd\u03b1 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03c4\u03bf\u03cd\u03bd \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c3\u03b5 contamination-free \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1. \u0397 \u03c0\u03b1\u03c1\u03bf\u03cd\u03c3\u03b1 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03ad\u03bd\u03b1 \u03bc\u03cc\u03bd\u03bf prompt, \u03c7\u03c9\u03c1\u03af\u03c2 \u03b5\u03bd\u03b1\u03bb\u03bb\u03b1\u03ba\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ad\u03c2 elicitation.<\/p>\n<p>\u0394\u03b5\u03bd \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03bb\u03ae\u03c6\u03b8\u03b7\u03ba\u03b1\u03bd \u03c0\u03bf\u03bb\u03cd \u03bc\u03b5\u03b3\u03ac\u03bb\u03b1 open-weight \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 70B \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd \u03ae \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf. \u0395\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd, \u03c4\u03b1 learned weights \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03c4\u03b7\u03ba\u03b1\u03bd \u03c3\u03c4\u03b1 160 \u03bc\u03b7 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac items, \u03cc\u03c0\u03bf\u03c5 \u03b7 contamination \u03b4\u03b7\u03bc\u03b9\u03bf\u03cd\u03c1\u03b3\u03b7\u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc error landscape \u03b1\u03c0\u03cc \u03b5\u03ba\u03b5\u03af\u03bd\u03bf \u03c4\u03c9\u03bd 94 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ce\u03bd items. \u03a4\u03bf \u03b3\u03b5\u03b3\u03bf\u03bd\u03cc\u03c2 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03c0\u03bb\u03b5\u03bf\u03bd\u03ad\u03ba\u03c4\u03b7\u03bc\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b8\u03b7\u03ba\u03b5 \u03c3\u03c4\u03bf clean subset \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03bd\u03b8\u03b1\u03c1\u03c1\u03c5\u03bd\u03c4\u03b9\u03ba\u03cc, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03b1 \u03af\u03b4\u03b9\u03b1 weights \u03b8\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03b5\u03af\u03bd\u03bf\u03c5\u03bd \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc dataset \u03ae \u03c0\u03bb\u03ae\u03c1\u03c9\u03c2 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ae \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7.<\/p>\n<p>\u03a5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 simultaneous aggregation \u03ba\u03b1\u03b9 \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c3\u03c5\u03bb\u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1. \u03a4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03b4\u03b1\u03bd \u03c4\u03b9\u03c2 \u03b5\u03ba\u03c4\u03b9\u03bc\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c4\u03c9\u03bd \u03ac\u03bb\u03bb\u03c9\u03bd, \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03ac\u03bb\u03bb\u03b1\u03be\u03b1\u03bd \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03b5\u03bd\u03b7\u03bc\u03b5\u03c1\u03ce\u03b8\u03b7\u03ba\u03b1\u03bd \u03b4\u03b9\u03b1\u03b4\u03bf\u03c7\u03b9\u03ba\u03ac. \u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03b5\u03b9 \u03c9\u03c2 \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ae \u03ba\u03b1\u03c4\u03b5\u03cd\u03b8\u03c5\u03bd\u03c3\u03b7 \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03b1 iterative deliberation, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03c0\u03b1\u03c1\u03ad\u03c7\u03b5\u03b9 \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03ad\u03c4\u03bf\u03b9\u03b1 \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b8\u03b1 \u03c0\u03bb\u03b7\u03c3\u03b9\u03ac\u03c3\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03bc\u03b9\u03b1\u03c2 prediction market.<\/p>\n<h2 id=\"ousia-diaforetikotita-lathon\">\u0397 \u03bf\u03c5\u03c3\u03af\u03b1: \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bb\u03b1\u03b8\u03ce\u03bd, \u03ba\u03b1\u03b8\u03b1\u03c1\u03cc\u03c2 \u03c7\u03c1\u03cc\u03bd\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03b7\u03bc\u03ad\u03bd\u03b7 \u03b5\u03bc\u03c0\u03b9\u03c3\u03c4\u03bf\u03c3\u03cd\u03bd\u03b7<\/h2>\n<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b4\u03af\u03bd\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03c0\u03b9\u03bf \u03ce\u03c1\u03b9\u03bc\u03b7 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf \u03b1\u03c0\u03bb\u03cc \u00ab\u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1\u00bb. \u03a4\u03b1 LLM ensembles \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03be\u03b5\u03c0\u03b5\u03c1\u03ac\u03c3\u03bf\u03c5\u03bd \u03c4\u03b1 \u03bc\u03ad\u03bb\u03b7 \u03c4\u03bf\u03c5\u03c2 \u03cc\u03c4\u03b1\u03bd \u03bf aggregator \u03bc\u03b1\u03b8\u03b1\u03af\u03bd\u03b5\u03b9 \u03c0\u03bf\u03b9\u03b1 error patterns \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03c9\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac. \u0397 logistic regression \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03cc\u03c4\u03b9 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03ba\u03ad\u03c1\u03b4\u03bf\u03c5\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03c1\u03bf\u03ba\u03cd\u03c8\u03b5\u03b9 \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1\u03bd \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03cd\u03c3\u03b9\u03bc\u03bf \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03cc \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03cc, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af\u03c4\u03b1\u03b9 \u03c3\u03cd\u03bd\u03b8\u03b5\u03c4\u03bf \u03bd\u03b5\u03c5\u03c1\u03c9\u03bd\u03b9\u03ba\u03cc \u03b4\u03af\u03ba\u03c4\u03c5\u03bf.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-label\">\u03a4\u03bf \u03ba\u03c1\u03b9\u03c4\u03ae\u03c1\u03b9\u03bf \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03ae\u03c2 \u03c7\u03c1\u03ae\u03c3\u03b7\u03c2<\/p>\n<p><strong>\u039a\u03c1\u03b1\u03c4\u03ae\u03c3\u03c4\u03b5 \u03c4\u03bf LLM ensemble \u03bc\u03cc\u03bd\u03bf \u03b1\u03bd \u03bd\u03b9\u03ba\u03ac \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac \u03c4\u03bf\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03bb\u03cc baseline \u03c3\u03b5 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ac \u03bc\u03b5\u03c4\u03b1\u03b3\u03b5\u03bd\u03ad\u03c3\u03c4\u03b5\u03c1\u03b1 cases.<\/strong><\/p>\n<p>\u0397 \u03c3\u03c5\u03bc\u03c6\u03c9\u03bd\u03af\u03b1 \u03c0\u03bf\u03bb\u03bb\u03ce\u03bd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03cc \u03bc\u03cc\u03bd\u03b7 \u03c4\u03b7\u03c2 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7. \u0397 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 Brier score \u03ae \u03ac\u03bb\u03bb\u03bf \u03c0\u03c1\u03bf\u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf metric, \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03b9\u03c9\u03bc\u03ad\u03bd\u03bf information cutoff, audit trail \u03ba\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 fallback \u03cc\u03c4\u03b1\u03bd \u03b7 \u03b4\u03b9\u03b1\u03c6\u03c9\u03bd\u03af\u03b1 \u03ae \u03b7 \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03be\u03b5\u03c0\u03b5\u03c1\u03bd\u03ac \u03c4\u03bf \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03cc \u03cc\u03c1\u03b9\u03bf.<\/p>\n<\/div>\n<p>\u03a4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03b1, \u03c4\u03bf contamination \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03ac\u03bd\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bd\u03b1 \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c0\u03bf\u03bb\u03cd \u03b9\u03ba\u03b1\u03bd\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03cc\u03c3\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ac \u03b3\u03b5\u03b3\u03bf\u03bd\u03cc\u03c4\u03b1. \u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 rankings \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03bf\u03b3\u03ba\u03ce\u03c3\u03b5\u03b9 \u03c4\u03bf \u03c7\u03ac\u03c3\u03bc\u03b1 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 cloud \u03ba\u03b1\u03b9 local \u03bb\u03cd\u03c3\u03b5\u03b9\u03c2. \u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7, \u03b1\u03c5\u03c4\u03cc \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf\u03c5 evaluation design \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03be\u03af\u03c3\u03bf\u03c5 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ae \u03bc\u03b5 \u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5.<\/p>\n<p>\u0397 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae \u03b1\u03be\u03af\u03b1 \u03b4\u03b5\u03bd \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03bc\u03b9\u03b1 \u03ad\u03c4\u03bf\u03b9\u03bc\u03b7 \u03bb\u03af\u03c3\u03c4\u03b1 weights \u03bf\u03cd\u03c4\u03b5 \u03c3\u03b5 \u03bc\u03b9\u03b1 \u03c5\u03c0\u03cc\u03c3\u03c7\u03b5\u03c3\u03b7 \u03cc\u03c4\u03b9 \u03c4\u03b1 LLMs \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03bf\u03cd\u03bd \u03c4\u03b7\u03bd \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03ba\u03c1\u03af\u03c3\u03b7. \u0392\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf: \u03bf\u03c1\u03b9\u03c3\u03bc\u03cc\u03c2 \u03ad\u03ba\u03b2\u03b1\u03c3\u03b7\u03c2, \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ac \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, \u03c0\u03b9\u03b8\u03b1\u03bd\u03bf\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2, \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 baselines, \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03c3\u03c6\u03b1\u03bb\u03bc\u03ac\u03c4\u03c9\u03bd \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc \u03ba\u03ac\u03b8\u03b5 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5. \u0397 \u03bf\u03bc\u03ac\u03b4\u03b1 AI \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03cc\u03c7\u03b9 \u03cc\u03c4\u03b1\u03bd \u03cc\u03bb\u03bf\u03b9 \u03bf\u03b9 \u03c8\u03b7\u03c6\u03b9\u03b1\u03ba\u03bf\u03af \u00ab\u03b5\u03b9\u03b4\u03b9\u03ba\u03bf\u03af\u00bb \u03c3\u03c5\u03bc\u03c6\u03c9\u03bd\u03bf\u03cd\u03bd, \u03b1\u03bb\u03bb\u03ac \u03cc\u03c4\u03b1\u03bd \u03bf\u03b9 \u03b4\u03b9\u03b1\u03c6\u03c9\u03bd\u03af\u03b5\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03ad\u03c7\u03bf\u03c5\u03bd \u03b5\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03cd\u03c3\u03b9\u03bc\u03b7, \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03c9\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-eyebrow\">\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 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd TWO DOTS<\/p>\n<p class=\"td-service-cta-title\">\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 multi-model workflows \u03c0\u03bf\u03c5 \u03bc\u03b5\u03c4\u03c1\u03bf\u03cd\u03bd \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c6\u03c9\u03bd\u03af\u03b1 \u03c0\u03c1\u03b9\u03bd \u03c4\u03b7\u03bd \u03ba\u03ac\u03bd\u03bf\u03c5\u03bd \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7.<\/p>\n<p>\u0397 TWO DOTS \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03b5\u03af \u03c4\u03b7\u03bd \u03ad\u03ba\u03b2\u03b1\u03c3\u03b7, \u03c4\u03b1 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, \u03c4\u03b1 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, \u03c4\u03b1 metrics, \u03c4\u03b1 fallbacks \u03ba\u03b1\u03b9 \u03c4\u03b1 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b1 checkpoints, \u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf LLM ensemble \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac business \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03b5 \u03b5\u03bd\u03c4\u03c5\u03c0\u03c9\u03c3\u03b9\u03b1\u03ba\u03ad\u03c2 \u03b1\u03bb\u03bb\u03ac \u03b1\u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03b5\u03c2 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 \u03c4\u03bf multi-model pilot \u03c3\u03b1\u03c2<\/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\">\u03a3\u03c5\u03c7\u03bd\u03ad\u03c2 \u03b5\u03c1\u03c9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2<\/p>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u00ab\u03c3\u03bf\u03c6\u03af\u03b1 \u03c4\u03bf\u03c5 \u03c0\u03bb\u03ae\u03b8\u03bf\u03c5\u03c2\u00bb \u03b3\u03b9\u03b1 \u03c4\u03b1 LLMs;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03b7 \u03c3\u03c5\u03bd\u03ac\u03b8\u03c1\u03bf\u03b9\u03c3\u03b7 \u03c4\u03c9\u03bd \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03c9\u03bd \u03c0\u03bf\u03bb\u03bb\u03ce\u03bd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ad\u03c3\u03c4\u03b5\u03c1\u03b7 \u03b1\u03c0\u03cc \u03c4\u03b9\u03c2 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03b9\u03c2 \u03c4\u03c9\u03bd \u03b5\u03c0\u03b9\u03bc\u03ad\u03c1\u03bf\u03c5\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd. \u03a3\u03c4\u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7, \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b8\u03b7\u03ba\u03b5 \u03ba\u03c5\u03c1\u03af\u03c9\u03c2 \u03bc\u03b5 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03c9\u03bd error patterns.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0391\u03c1\u03ba\u03b5\u03af \u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc\u03c2 \u03bc\u03ad\u03c3\u03bf\u03c2 \u03c4\u03c9\u03bd \u03c0\u03b9\u03b8\u03b1\u03bd\u03bf\u03c4\u03ae\u03c4\u03c9\u03bd;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0389\u03c4\u03b1\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf\u03c2 \u03b1\u03c0\u03cc \u03b1\u03c1\u03ba\u03b5\u03c4\u03ad\u03c2 \u03ac\u03bb\u03bb\u03b5\u03c2 \u03ba\u03bb\u03b1\u03c3\u03b9\u03ba\u03ad\u03c2 \u03bc\u03b5\u03b8\u03cc\u03b4\u03bf\u03c5\u03c2 \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf clean subset, \u03b1\u03bb\u03bb\u03ac \u03c5\u03c3\u03c4\u03b5\u03c1\u03bf\u03cd\u03c3\u03b5 \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 logistic regression \u03ba\u03b1\u03b9 MLP. \u0394\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ac \u03ba\u03b1\u03ba\u03ae \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae, \u03cc\u03bc\u03c9\u03c2 \u03b4\u03b5\u03bd \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03ac \u03bc\u03bf\u03c4\u03af\u03b2\u03b1 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03c9\u03bc\u03b1\u03c4\u03b9\u03ba\u03ce\u03bd \u03bb\u03b1\u03b8\u03ce\u03bd.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 logistic regression \u03b1\u03c0\u03ad\u03b4\u03c9\u03c3\u03b5 \u03c4\u03cc\u03c3\u03bf \u03ba\u03b1\u03bb\u03ac;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03c0\u03b5\u03b9\u03b4\u03ae \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b5 \u03bd\u03b1 \u03bc\u03ac\u03b8\u03b5\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac, \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03ac, weights \u03b3\u03b9\u03b1 \u03c4\u03b1 15 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1. \u03a4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03b7 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03ae \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ce\u03bd error patterns \u03c0\u03b1\u03c1\u03b5\u03af\u03c7\u03b5 \u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03bf\u03c6\u03ad\u03bb\u03bf\u03c5\u03c2.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf training cutoff contamination;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03bf \u03ba\u03af\u03bd\u03b4\u03c5\u03bd\u03bf\u03c2 \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bd\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03c4\u03b5\u03af \u03c3\u03b5 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03bf\u03c5\u03bd \u03ae\u03b4\u03b7 \u03c4\u03b7\u03bd \u03ad\u03ba\u03b2\u03b1\u03c3\u03b7 \u03b5\u03bd\u03cc\u03c2 \u03b5\u03c1\u03c9\u03c4\u03ae\u03bc\u03b1\u03c4\u03bf\u03c2. \u03a3\u03b5 \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7\u03bd \u03c0\u03b5\u03c1\u03af\u03c0\u03c4\u03c9\u03c3\u03b7 \u03b7 \u03c6\u03b1\u03b9\u03bd\u03bf\u03bc\u03b5\u03bd\u03b9\u03ba\u03ae \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03ac\u03ba\u03bb\u03b7\u03c3\u03b7 \u03b3\u03bd\u03c9\u03c3\u03c4\u03bf\u03cd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03bf\u03c2.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b1 cloud \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ae\u03c4\u03b1\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b1 local;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03c4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 dataset \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03c3\u03c4\u03b7\u03ba\u03b5 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf \u03c7\u03ac\u03c3\u03bc\u03b1, \u03b1\u03bb\u03bb\u03ac \u03bc\u03b5 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc \u03bc\u03ad\u03c3\u03bf \u03bc\u03b5\u03b9\u03ce\u03b8\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc 35,8% \u03c3\u03b5 8,9% \u03c3\u03c4\u03bf clean subset. \u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03b1\u03c1\u03c7\u03b9\u03ba\u03ae\u03c2 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac\u03c2 \u03c3\u03c7\u03b5\u03c4\u03b9\u03b6\u03cc\u03c4\u03b1\u03bd \u03bc\u03b5 contamination.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03ad\u03bd\u03b1 \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ac \u03b1\u03b4\u03cd\u03bd\u03b1\u03bc\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bd\u03b1 \u03b2\u03bf\u03b7\u03b8\u03ae\u03c3\u03b5\u03b9 \u03ad\u03bd\u03b1 ensemble;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039d\u03b1\u03b9, \u03b1\u03bd \u03c4\u03b1 \u03bb\u03ac\u03b8\u03b7 \u03c4\u03bf\u03c5 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03b1\u03c0\u03cc \u03c4\u03b1 \u03bb\u03ac\u03b8\u03b7 \u03c4\u03c9\u03bd \u03ac\u03bb\u03bb\u03c9\u03bd. \u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b2\u03c1\u03ae\u03ba\u03b5 \u03cc\u03c4\u03b9 \u03b7 \u03b2\u03b1\u03c1\u03cd\u03c4\u03b7\u03c4\u03b1 \u03c3\u03c4\u03b7 logistic regression \u03c3\u03c5\u03bd\u03b4\u03b5\u03cc\u03c4\u03b1\u03bd \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03bc\u03b5 error diversity \u03c0\u03b1\u03c1\u03ac \u03bc\u03b5 \u03b1\u03c4\u03bf\u03bc\u03b9\u03ba\u03cc performance rank.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u039e\u03b5\u03c0\u03ad\u03c1\u03b1\u03c3\u03b1\u03bd \u03c4\u03b1 LLMs \u03c4\u03b7\u03bd \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 prediction market;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0391\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03bc\u03b5 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ac \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03b9\u03c3\u03bc\u03ad\u03bd\u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7, \u03c4\u03b1 LLMs \u03b5\u03af\u03c7\u03b1\u03bd \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ac \u03c7\u03b5\u03b9\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf Brier score. \u0397 \u03b1\u03b3\u03bf\u03c1\u03ac \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03c3\u03b5 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7, \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03c5\u03bc\u03ad\u03bd\u03b7 \u03b3\u03bd\u03ce\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03b5\u03c7\u03ae \u03b5\u03bd\u03b7\u03bc\u03ad\u03c1\u03c9\u03c3\u03b7.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03ce\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7 \u03bd\u03b1 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03b5\u03b9 multi-model forecasting;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039c\u03b5 \u03bc\u03af\u03b1 \u03c3\u03b1\u03c6\u03ae \u03ad\u03ba\u03b2\u03b1\u03c3\u03b7, \u03af\u03b4\u03b9\u03b1 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03ba\u03b1\u03b9 prompt \u03b3\u03b9\u03b1 \u03cc\u03bb\u03b1 \u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03cc train\/test split, \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03b1\u03c6\u03ae \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03c9\u03bd \u03ba\u03b1\u03b9 cutoffs, \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03bc\u03b5 \u03b1\u03c0\u03bb\u03ac baselines \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc \u03ba\u03ac\u03b8\u03b5 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae. \u03a4\u03b1 weights \u03c4\u03b7\u03c2 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7\u03c2 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7\u03c2 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03bd\u03c4\u03b9\u03b3\u03c1\u03b1\u03c6\u03bf\u03cd\u03bd \u03c3\u03b5 \u03ac\u03bb\u03bb\u03bf domain \u03c7\u03c9\u03c1\u03af\u03c2 validation.<\/p>\n<\/div>\n<\/details>\n<\/div>\n<\/section>\n<div class=\"td-source-list\">\n<p id=\"piges\" class=\"td-source-list-title\">\u03a0\u03b7\u03b3\u03ad\u03c2<\/p>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2607.18269\" target=\"_blank\" rel=\"noopener\">Wisdom of LLM Crowds: Aggregation and Contamination in Language Model Ensembles, arXiv:2607.18269v2<\/a><\/li>\n<li><a href=\"https:\/\/airc.nist.gov\/airmf-resources\/airmf\/5-sec-core\/\" target=\"_blank\" rel=\"noopener\">NIST AI Risk Management Framework Core, Measure<\/a><\/li>\n<li><a href=\"https:\/\/www.nist.gov\/caisi\/cheating-ai-agent-evaluations\/2-examples-cheating-caisis-agent-evaluations\" target=\"_blank\" rel=\"noopener\">NIST CAISI: contamination in AI evaluations<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2402.19379\" target=\"_blank\" rel=\"noopener\">Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracy<\/a><\/li>\n<\/ul>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u039c\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03c0\u03bf\u03bb\u03bb\u03ac LLMs \u03bd\u03b1 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03bf\u03c5\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03bc\u03b1\u03b6\u03af; \u0397 \u03ad\u03c1\u03b5\u03c5\u03bd\u03b1 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03c0\u03cc\u03c4\u03b5 \u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bb\u03b1\u03b8\u03ce\u03bd \u03b2\u03bf\u03b7\u03b8\u03ac \u03ba\u03b1\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf training cutoff contamination \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7.<\/p>","protected":false},"author":1,"featured_media":87087,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[17366],"tags":[9261,17370,17931,9529,3597],"class_list":["post-87071","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-marketing","tag-ai-strategy","tag-data-quality","tag-llms","tag-predictive-analytics","tag-techniti-noimosyni"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/87071","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=87071"}],"version-history":[{"count":0,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/87071\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media\/87087"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media?parent=87071"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/categories?post=87071"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/tags?post=87071"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}