{"id":98242,"date":"2026-10-05T18:38:05","date_gmt":"2026-10-05T15:38:05","guid":{"rendered":"https:\/\/twodots.gr\/?p=98242"},"modified":"2026-10-05T18:38:07","modified_gmt":"2026-10-05T15:38:07","slug":"data-mixing-llms-peirama-migmatos","status":"publish","type":"post","link":"https:\/\/twodots.gr\/en\/data-mixing-llms-peirama-migmatos\/","title":{"rendered":"Data mixing \u03c3\u03c4\u03b1 LLMs: \u03b3\u03b9\u03b1\u03c4\u03af \u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03c3\u03cd\u03bd\u03b8\u03b5\u03c3\u03b7 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1, \u03cc\u03c7\u03b9 \u03b1\u03c0\u03bb\u03ae \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>Answer first:<\/strong> \u03a4\u03bf data mixing \u03c3\u03c4\u03b1 LLMs \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ae \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u00ab\u03ba\u03b1\u03bb\u03ce\u03bd\u00bb datasets. \u039c\u03b5 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc token budget, \u03ba\u03ac\u03b8\u03b5 \u03b1\u03cd\u03be\u03b7\u03c3\u03b7 \u03b5\u03bd\u03cc\u03c2 domain \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf \u03bc\u03b5\u03c1\u03af\u03b4\u03b9\u03bf \u03c4\u03c9\u03bd \u03ac\u03bb\u03bb\u03c9\u03bd, \u03b5\u03bd\u03ce \u03b7 \u03b1\u03be\u03af\u03b1 \u03bc\u03b9\u03b1\u03c2 \u03c0\u03b7\u03b3\u03ae\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03b1\u03bd\u03ac\u03bb\u03bf\u03b3\u03b1 \u03bc\u03b5 \u03c4\u03bf \u03c4\u03b9 \u03c4\u03b7 \u03c3\u03c5\u03bd\u03bf\u03b4\u03b5\u03cd\u03b5\u03b9. \u0393\u03b9\u2019 \u03b1\u03c5\u03c4\u03cc \u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03c3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 mixture experiment, \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b1 proxy runs, \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03b9\u03b4\u03c1\u03ac\u03c3\u03b5\u03c9\u03bd \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ad\u03c2 \u03ba\u03c1\u03b9\u03c4\u03ae\u03c1\u03b9\u03bf \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf scale-up.<\/p>\n<\/div>\n<p>\u03a4\u03bf data mixing \u03c3\u03c4\u03b1 LLMs \u03c9\u03c2 \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03bc\u03af\u03b3\u03bc\u03b1\u03c4\u03bf\u03c2: \u03c0\u03ce\u03c2 Scheff\u00e9 models \u03ba\u03b1\u03b9 I-optimal design \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03bf\u03c5\u03bd proxy runs, \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2, ranking \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2 scale-up.<\/p>\n<p>\u038c\u03c4\u03b1\u03bd \u03bc\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf \u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03b5 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03c0\u03c1\u03bf\u03cb\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03cc tokens, \u03b4\u03b5\u03bd \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c3\u03b5\u03b9 \u00ab\u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03cc\u03bb\u03b1\u00bb. \u03a0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf\u03c2 \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1\u03c2 \u03b1\u03c6\u03ae\u03bd\u03b5\u03b9 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03bf \u03c7\u03ce\u03c1\u03bf \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c3\u03c4\u03b7\u03bc\u03bf\u03bd\u03b9\u03ba\u03ac \u03ac\u03c1\u03b8\u03c1\u03b1, \u03b2\u03b9\u03b2\u03bb\u03af\u03b1, \u03bd\u03bf\u03bc\u03b9\u03ba\u03cc \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf \u03ae \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc web content. \u0397 \u03c0\u03c1\u03bf\u03b4\u03b7\u03bc\u03bf\u03c3\u03af\u03b5\u03c5\u03c3\u03b7 <strong>Data Mixing as Mixture Experiment<\/strong> \u03c4\u03c9\u03bd Yicheng Mao \u03ba\u03b1\u03b9 Hongru Du \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b1\u03c5\u03c4\u03ae \u03b7 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03ad\u03c7\u03b5\u03b9 \u03c4\u03b7 \u03b4\u03bf\u03bc\u03ae \u03b5\u03bd\u03cc\u03c2 \u03ba\u03bb\u03b1\u03c3\u03b9\u03ba\u03bf\u03cd \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03bf\u03c2 \u03bc\u03af\u03b3\u03bc\u03b1\u03c4\u03bf\u03c2 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc \u03c0\u03cc\u03c1\u03bf.<\/p>\n<p>\u0397 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae. \u0394\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03bd\u03b1 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03c4\u03bf\u03cd\u03bd \u03bc\u03b9\u03ba\u03c1\u03ac proxy models \u03c3\u03b5 \u03c4\u03c5\u03c7\u03b1\u03af\u03bf\u03c5\u03c2 \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03bf\u03cd\u03c2 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b7\u03b8\u03b5\u03af \u03ad\u03bd\u03b1\u03c2 predictor \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03b7 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b1. \u03a0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c6\u03b1\u03c3\u03b9\u03c3\u03c4\u03b5\u03af \u03ba\u03b1\u03b9 <em>who<\/em> \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03bf\u03af \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03bf\u03ba\u03b9\u03bc\u03b1\u03c3\u03c4\u03bf\u03cd\u03bd, \u03ce\u03c3\u03c4\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03b1\u03ba\u03c1\u03b9\u03b2\u03cc run \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1. \u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03b9 \u03c4\u03bf LLM pretraining \u03bc\u03b5 Scheff\u00e9 response surfaces \u03ba\u03b1\u03b9 model-robust I-optimal \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03cc, \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ce\u03bd\u03c4\u03b1\u03c2 \u03c4\u03b1 \u03b4\u03b7\u03bc\u03cc\u03c3\u03b9\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03c4\u03bf\u03c5 RegMix \u03c9\u03c2 case study.<\/p>\n<aside class=\"td-article-note\"><strong>\u0397 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7:<\/strong> \u03c4\u03b1 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ac \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b1\u03c6\u03bf\u03c1\u03bf\u03cd\u03bd \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf RegMix dataset, \u03c4\u03bf Pile-CC validation objective \u03ba\u03b1\u03b9 simulation \u03b2\u03b1\u03b8\u03bc\u03bf\u03bd\u03bf\u03bc\u03b7\u03bc\u03ad\u03bd\u03bf \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03b7 fitted \u03b5\u03c0\u03b9\u03c6\u03ac\u03bd\u03b5\u03b9\u03b1. \u03a4\u03bf \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 25% \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b1 proxy runs \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ae \u03c5\u03c0\u03cc\u03c3\u03c7\u03b5\u03c3\u03b7 \u03b5\u03be\u03bf\u03b9\u03ba\u03bf\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7\u03c2 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 LLM project.<\/aside>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">Contents<\/div>\n<ul>\n<li><a href=\"#mixture-experiment\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf data mixing \u03b5\u03af\u03bd\u03b1\u03b9 mixture experiment<\/a><\/li>\n<li><a href=\"#regmix-case-study\">\u03a4\u03bf RegMix case study \u03c4\u03c9\u03bd 512 proxy runs<\/a><\/li>\n<li><a href=\"#scheffe-interactions\">\u03a4\u03b9 \u03b1\u03c0\u03bf\u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 \u03c4\u03bf Scheff\u00e9 \u03b3\u03b9\u03b1 additive effects \u03ba\u03b1\u03b9 interactions<\/a><\/li>\n<li><a href=\"#relational-domain-value\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 \u03b1\u03be\u03af\u03b1 \u03b5\u03bd\u03cc\u03c2 domain \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c7\u03b5\u03c3\u03b9\u03b1\u03ba\u03ae<\/a><\/li>\n<li><a href=\"#rank-preservation\">\u03a0\u03ce\u03c2 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b7 \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03b1\u03c0\u03cc 1M \u03ad\u03c9\u03c2 1B<\/a><\/li>\n<li><a href=\"#i-optimal-design\">\u03a0\u03bf\u03b9\u03b1 proxy runs \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03c4\u03bf\u03cd\u03bd<\/a><\/li>\n<li><a href=\"#simulation-saving\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 25% \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b1 runs<\/a><\/li>\n<li><a href=\"#business-translation\">\u0397 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03bc\u03b5\u03c4\u03ac\u03c6\u03c1\u03b1\u03c3\u03b7 \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf pretraining<\/a><\/li>\n<li><a href=\"#praktiko-protokollo\">\u0395\u03c0\u03c4\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03b5\u03bb\u03ad\u03b3\u03be\u03b9\u03bc\u03bf data-mixing experiment<\/a><\/li>\n<li><a href=\"#periorismoi-governance\">\u03a0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af, data governance \u03ba\u03b1\u03b9 \u03bb\u03ac\u03b8\u03bf\u03c2 objectives<\/a><\/li>\n<li><a href=\"#stratigiki-dedomenon\">\u0391\u03c0\u03cc \u03c4\u03b7 \u03c3\u03c5\u03bd\u03c4\u03b1\u03b3\u03ae \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03c3\u03c4\u03b7 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae \u03bc\u03ac\u03b8\u03b7\u03c3\u03b7\u03c2<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"mixture-experiment\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf data mixing \u03b5\u03af\u03bd\u03b1\u03b9 mixture experiment<\/h2>\n<p>\u03a3\u03b5 \u03ad\u03bd\u03b1 \u03c3\u03c5\u03bd\u03b7\u03b8\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf regression problem, \u03bf\u03b9 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03ad\u03c2 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03c3\u03c5\u03c7\u03bd\u03ac \u03bd\u03b1 \u03b1\u03c5\u03be\u03b7\u03b8\u03bf\u03cd\u03bd \u03ae \u03bd\u03b1 \u03bc\u03b5\u03b9\u03c9\u03b8\u03bf\u03cd\u03bd \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b1. \u03a3\u03c4\u03bf data mixing \u03bf\u03b9 \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b5\u03c2 \u03c4\u03c9\u03bd domains \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b7 \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03bf \u03ac\u03b8\u03c1\u03bf\u03b9\u03c3\u03bc\u03ac \u03c4\u03bf\u03c5\u03c2 \u03b9\u03c3\u03bf\u03cd\u03c4\u03b1\u03b9 \u03c0\u03ac\u03bd\u03c4\u03b1 \u03bc\u03b5 \u03ad\u03bd\u03b1. \u038c\u03bb\u03bf\u03b9 \u03bf\u03b9 \u03b5\u03c6\u03b9\u03ba\u03c4\u03bf\u03af \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03bf\u03af \u03b2\u03c1\u03af\u03c3\u03ba\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03ad\u03bd\u03b1 probability simplex: \u03b7 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bf\u03bb\u03ae \u03b5\u03bd\u03cc\u03c2 \u03c3\u03c5\u03c3\u03c4\u03b1\u03c4\u03b9\u03ba\u03bf\u03cd \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c5\u03c0\u03bf\u03c7\u03c1\u03b5\u03c9\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf\u03bd \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03bf \u03c7\u03ce\u03c1\u03bf \u03b3\u03b9\u03b1 \u03c4\u03b1 \u03c5\u03c0\u03cc\u03bb\u03bf\u03b9\u03c0\u03b1.<\/p>\n<p>\u0397 \u03af\u03b4\u03b9\u03b1 \u03b3\u03b5\u03c9\u03bc\u03b5\u03c4\u03c1\u03af\u03b1 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1 \u03c4\u03c1\u03bf\u03c6\u03af\u03bc\u03c9\u03bd, \u03c7\u03b7\u03bc\u03b9\u03ba\u03ce\u03bd \u03c3\u03c5\u03bd\u03b8\u03ad\u03c3\u03b5\u03c9\u03bd \u03ae media mix, \u03cc\u03c0\u03bf\u03c5 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7 \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03b5\u03b9 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc budget \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03c3\u03c5\u03c3\u03c4\u03b1\u03c4\u03b9\u03ba\u03ac \u03ae \u03ba\u03b1\u03bd\u03ac\u03bb\u03b9\u03b1. \u03a3\u03c4\u03bf LLM pretraining, \u03c4\u03b1 \u03c3\u03c5\u03c3\u03c4\u03b1\u03c4\u03b9\u03ba\u03ac \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03b1 data domains, \u03bf\u03b9 \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b5\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03b1 token shares, \u03c4\u03b1 proxy runs \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03b1 experimental design points \u03ba\u03b1\u03b9 \u03b7 validation loss \u03ae \u03b7 downstream \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03c3\u03c7\u03b7\u03bc\u03b1\u03c4\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03c6\u03ac\u03bd\u03b5\u03b9\u03b1 \u03b1\u03c0\u03cc\u03ba\u03c1\u03b9\u03c3\u03b7\u03c2.<\/p>\n<p>\u0397 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03ba\u03b1\u03b4\u03b7\u03bc\u03b1\u03ca\u03ba\u03ae \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03ad\u03c1\u03b5\u03b9\u03b1. \u0391\u03bd \u03c4\u03b1 \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03ac \u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03b9\u03c3\u03c4\u03bf\u03cd\u03bd \u03c3\u03b1\u03bd \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b1 features, \u03b7 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03c1\u03cd\u03c8\u03b5\u03b9 \u03c4\u03bf\u03bd compositional \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03cc \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03c5\u03c0\u03b5\u03c1\u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03cd\u03c3\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03bc\u03b5\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03b7 \u03c0\u03b7\u03b3\u03ae. \u0388\u03bd\u03b1 mixture model \u03b5\u03bd\u03c3\u03c9\u03bc\u03b1\u03c4\u03ce\u03bd\u03b5\u03b9 \u03b5\u03be\u03b1\u03c1\u03c7\u03ae\u03c2 \u03cc\u03c4\u03b9 \u03b7 \u03b5\u03c1\u03ce\u03c4\u03b7\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u00ab\u03c0\u03bf\u03b9\u03bf dataset \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03bb\u03cc;\u00bb, \u03b1\u03bb\u03bb\u03ac \u00ab\u03c0\u03bf\u03b9\u03b1 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03c4\u03bf\u03c5 \u03af\u03b4\u03b9\u03bf\u03c5 budget \u03b5\u03be\u03c5\u03c0\u03b7\u03c1\u03b5\u03c4\u03b5\u03af \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03c4\u03bf \u03b5\u03c0\u03b9\u03bb\u03b5\u03b3\u03bc\u03ad\u03bd\u03bf objective;\u00bb.<\/p>\n<div class=\"td-comparison td-comparison-cards td-comparison-cards--horizontal\">\n<div class=\"td-comparison-grid td-comparison-grid-three\">\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">Random sampling<\/p>\n<p>\u0394\u03af\u03bd\u03b5\u03b9 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b1 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac mixtures, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03b5\u03b3\u03b3\u03c5\u03ac\u03c4\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03b1 \u03c3\u03b7\u03bc\u03b5\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03b1 \u03c0\u03b9\u03bf \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03b9\u03b1\u03ba\u03ac \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03c6\u03ac\u03bd\u03b5\u03b9\u03b1 \u03b1\u03c0\u03cc\u03ba\u03c1\u03b9\u03c3\u03b7\u03c2.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">Dirichlet<\/span><span class=\"td-badge\">Ease<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<p class=\"td-comparison-title\">I-optimal design<\/p>\n<p>\u0395\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03b9 mixtures \u03c0\u03bf\u03c5 \u03bc\u03b5\u03b9\u03ce\u03bd\u03bf\u03c5\u03bd \u03c4\u03b7 \u03bc\u03ad\u03c3\u03b7 prediction variance \u03c3\u03c4\u03b7\u03bd \u03b5\u03c6\u03b9\u03ba\u03c4\u03ae \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ae, \u03bc\u03b5 \u03c3\u03c4\u03cc\u03c7\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 unseen mixtures.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">Information<\/span><span class=\"td-badge\">Simplex<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">Model-robust design<\/p>\n<p>\u0399\u03c3\u03bf\u03c1\u03c1\u03bf\u03c0\u03b5\u03af first-order \u03ba\u03b1\u03b9 second-order Scheff\u00e9 \u03c5\u03c0\u03bf\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2 \u03cc\u03c4\u03b1\u03bd \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bc\u03b5 \u03c0\u03cc\u03c3\u03bf \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ac \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03b1 interactions.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u0391\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1<\/span><span class=\"td-badge\">Interactions<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"regmix-case-study\">\u03a4\u03bf RegMix case study \u03c4\u03c9\u03bd 512 proxy runs<\/h2>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b4\u03b5\u03bd \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03bf\u03cd\u03bd \u03bd\u03ad\u03bf \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf corpus. \u0395\u03c0\u03b1\u03bd\u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf main experiment \u03c4\u03bf\u03c5 RegMix, \u03c4\u03bf \u03bf\u03c0\u03bf\u03af\u03bf \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 17 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b1 domains \u03b1\u03c0\u03cc \u03c4\u03bf Pile. \u0391\u03bd\u03ac\u03bc\u03b5\u03c3\u03ac \u03c4\u03bf\u03c5\u03c2 \u03b2\u03c1\u03af\u03c3\u03ba\u03bf\u03bd\u03c4\u03b1\u03b9 arXiv, GitHub, Wikipedia, Stack Exchange, PubMed, FreeLaw, Enron Emails, Gutenberg, Hacker News, Pile-CC \u03ba\u03b1\u03b9 \u03ac\u03bb\u03bb\u03b1 \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03c5\u03bc\u03ad\u03bd\u03b1 \u03c3\u03cd\u03bd\u03bf\u03bb\u03b1.<\/p>\n<p>\u03a3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf setup, \u03c4\u03bf RegMix \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b5 512 proxy models \u03bc\u03b5 1 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03bf non-embedding parameters \u03ba\u03b1\u03b9 1 \u03b4\u03b9\u03c3\u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03bf tokens \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 run. \u03a3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b5 \u03b1\u03bd \u03bf\u03b9 fitted predictors \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03bf\u03cd\u03bd \u03c4\u03b7 \u03c3\u03b5\u03b9\u03c1\u03ac \u03c4\u03c9\u03bd mixtures \u03c3\u03b5 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b5\u03c2 1M, 60M \u03ba\u03b1\u03b9 1B. \u0397 \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7: \u03bf proxy \u03b4\u03b5\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03bd\u03b1 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03b1\u03c0\u03cc\u03bb\u03c5\u03c4\u03b7 loss, \u03b1\u03bb\u03bb\u03ac \u03bd\u03b1 \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03c0\u03bf\u03b9\u03b1 \u03c3\u03cd\u03bd\u03b8\u03b5\u03c3\u03b7 \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03c0\u03cc \u03bc\u03b9\u03b1 \u03ac\u03bb\u03bb\u03b7.<\/p>\n<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ae\u03c2 \u03cc\u03c4\u03b9 \u03c4\u03b1 \u03b5\u03bc\u03c0\u03b5\u03b9\u03c1\u03b9\u03ba\u03ac \u03b5\u03c5\u03c1\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03bd\u03ae\u03ba\u03bf\u03c5\u03bd \u03c3\u03b5 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf dataset, taxonomy \u03ba\u03b1\u03b9 objective. \u0394\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03bf\u03b9 \u03af\u03b4\u03b9\u03b5\u03c2 \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b5\u03c2 \u03ae \u03bf\u03b9 \u03af\u03b4\u03b9\u03b5\u03c2 \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03b9\u03b4\u03c1\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b8\u03b1 \u03b5\u03bc\u03c6\u03b1\u03bd\u03b9\u03c3\u03c4\u03bf\u03cd\u03bd \u03c3\u03b5 \u03ac\u03bb\u03bb\u03bf corpus. \u0397 \u03b1\u03be\u03af\u03b1 \u03c4\u03bf\u03c5 paper \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03bf\u03cd, \u03cc\u03c7\u03b9 \u03c3\u03b5 \u03bc\u03b9\u03b1 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ae \u00ab\u03c3\u03c5\u03bd\u03c4\u03b1\u03b3\u03ae\u00bb \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<div class=\"td-chart-head\">\n<p class=\"td-chart-kicker\">\u03a4\u03b5\u03ba\u03bc\u03b7\u03c1\u03b9\u03c9\u03bc\u03ad\u03bd\u03bf scope<\/p>\n<p class=\"td-chart-title\">\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b1 \u03bc\u03b5\u03b3\u03ad\u03b8\u03b7 \u03c4\u03bf\u03c5 RegMix case study<\/p>\n<p class=\"td-chart-intro\">\u039f\u03b9 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03bf\u03c5\u03bd \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03b5\u03c0\u03b1\u03bd\u03b5\u03be\u03b5\u03c4\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5. \u0394\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 benchmark \u03b1\u03b3\u03bf\u03c1\u03ac\u03c2, ROI \u03ae \u03c0\u03c1\u03cc\u03c4\u03b1\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03c4\u03bf \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2 \u03ba\u03ac\u03b8\u03b5 \u03bd\u03ad\u03bf\u03c5 LLM project.<\/p>\n<\/div>\n<div class=\"td-metric-grid\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">17 domains<\/span><span class=\"td-metric-label\">\u0394\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b5\u03c2 \u03c0\u03b7\u03b3\u03ad\u03c2 \u03c4\u03bf\u03c5 Pile \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf analyzed mixture space<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">512 runs<\/span><span class=\"td-metric-label\">Proxy mixtures \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03b1 \u03bf\u03c0\u03bf\u03af\u03b1 fitted \u03c4\u03b1 response models<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">1 \u03b4\u03b9\u03c3. tokens\/run<\/span><span class=\"td-metric-label\">Training budget \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 proxy model 1M non-embedding parameters<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">3 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b5\u03c2<\/span><span class=\"td-metric-label\">Held-out \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 mixtures \u03c3\u03c4\u03b1 1M, 60M \u03ba\u03b1\u03b9 1B<\/span><\/div>\n<\/div>\n<\/div>\n<h2 id=\"scheffe-interactions\">\u03a4\u03b9 \u03b1\u03c0\u03bf\u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 \u03c4\u03bf Scheff\u00e9 \u03b3\u03b9\u03b1 additive effects \u03ba\u03b1\u03b9 interactions<\/h2>\n<p>\u0388\u03bd\u03b1 first-order Scheff\u00e9 model \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03c0\u03cc\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c9\u03c2 \u03c3\u03c4\u03b1\u03b8\u03bc\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf \u03ac\u03b8\u03c1\u03bf\u03b9\u03c3\u03bc\u03b1 \u03c4\u03c9\u03bd domain proportions \u03c7\u03c9\u03c1\u03af\u03c2 \u03ba\u03bf\u03b9\u03bd\u03cc intercept, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03c4\u03bf \u03ac\u03b8\u03c1\u03bf\u03b9\u03c3\u03bc\u03b1 \u03c4\u03c9\u03bd \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03b9\u03ce\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03ae\u03b4\u03b7 \u03ad\u03bd\u03b1. \u039f first-order \u03c3\u03c5\u03bd\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03ae\u03c2 \u03b5\u03bd\u03cc\u03c2 domain \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af \u03c3\u03c4\u03b7 fitted \u03b1\u03c0\u03cc\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c3\u03c4\u03b7 \u03b3\u03c9\u03bd\u03af\u03b1 \u03c4\u03bf\u03c5 simplex \u03c0\u03bf\u03c5 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b9\u03c3\u03c4\u03ac \u03c4\u03bf \u03ba\u03b1\u03b8\u03b1\u03c1\u03cc \u03c3\u03c5\u03c3\u03c4\u03b1\u03c4\u03b9\u03ba\u03cc. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c0\u03c1\u03bf\u03b5\u03b9\u03b4\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd \u03cc\u03c4\u03b9 \u03c0\u03c1\u03cc\u03ba\u03b5\u03b9\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 model-based interpretation \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b3\u03b9\u03b1 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae\u03c2 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7\u03c2 \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf domain.<\/p>\n<p>\u0397 \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03b7 \u03c4\u03ac\u03be\u03b7 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 \u03cc\u03c1\u03bf\u03c5\u03c2 \u03b1\u03bd\u03ac \u03b6\u03b5\u03cd\u03b3\u03bf\u03c2. \u0395\u03c6\u03cc\u03c3\u03bf\u03bd \u03b7 \u03b1\u03c0\u03cc\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 validation loss, \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03cc\u03c2 interaction coefficient \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03bf \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03cc\u03c2 \u03b4\u03cd\u03bf domains \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7 fitted loss \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03cc\u03c3\u03bf \u03b1\u03bd\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03c4\u03bf additive baseline. \u0398\u03b5\u03c4\u03b9\u03ba\u03cc\u03c2 \u03c3\u03c5\u03bd\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03ae\u03c2 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 loss \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd additive \u03c0\u03c1\u03bf\u03c3\u03b4\u03bf\u03ba\u03af\u03b1. \u0388\u03c4\u03c3\u03b9 \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bf\u03c1\u03b1\u03c4\u03cc \u03b1\u03bd \u03b4\u03cd\u03bf \u03c0\u03b7\u03b3\u03ad\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03c9\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ae \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03c4\u03bf\u03bd \u03af\u03b4\u03b9\u03bf \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf \u03c7\u03ce\u03c1\u03bf.<\/p>\n<p>\u039c\u03b5 17 domains, \u03c4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 second-order \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ad\u03c7\u03b5\u03b9 153 \u03cc\u03c1\u03bf\u03c5\u03c2: 17 first-order \u03ba\u03b1\u03b9 136 pairwise interactions. \u0393\u03b9\u03b1 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c6\u03b5\u03c5\u03c7\u03b8\u03b5\u03af \u03ad\u03bd\u03b1\u03c2 \u03b4\u03cd\u03c3\u03c7\u03c1\u03b7\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03c3\u03c4\u03b1\u03b8\u03ae\u03c2 \u03c0\u03af\u03bd\u03b1\u03ba\u03b1\u03c2, \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03b6\u03b5\u03b9 L1 penalty \u03bc\u03b5 \u03c0\u03b1\u03c1\u03ac\u03bc\u03b5\u03c4\u03c1\u03bf \u03c0\u03bf\u03c5 \u03b5\u03c0\u03b9\u03bb\u03ad\u03c7\u03b8\u03b7\u03ba\u03b5 \u03bc\u03ad\u03c3\u03c9 10-fold cross-validation. \u03a4\u03bf sparse \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ba\u03c1\u03ac\u03c4\u03b7\u03c3\u03b5 80 \u03cc\u03c1\u03bf\u03c5\u03c2, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae \u03cc\u03bb\u03bf\u03c5\u03c2 \u03c4\u03bf\u03c5\u03c2 17 first-order \u03ba\u03b1\u03b9 63 interactions.<\/p>\n<p>\u0397 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c0\u03c1\u03ce\u03c4\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03b7\u03c2 \u03c4\u03ac\u03be\u03b7\u03c2 \u03ad\u03b4\u03c9\u03c3\u03b5 F=6,5145 \u03bc\u03b5 136 interaction \u03ba\u03b1\u03b9 359 residual degrees of freedom, \u03bc\u03b5 p&lt;0,001. \u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03bf\u03b9 pairwise \u03cc\u03c1\u03bf\u03b9 \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b1\u03bd \u03c4\u03b7 fitted \u03b5\u03c0\u03b9\u03c6\u03ac\u03bd\u03b5\u03b9\u03b1 \u03c9\u03c2 \u03bf\u03bc\u03ac\u03b4\u03b1\u00b7 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03cc significance test \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03c3\u03c5\u03bd\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03ae \u03bf\u03cd\u03c4\u03b5 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03b1\u03b9\u03c4\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03b4\u03cd\u03bf datasets.<\/p>\n<h2 id=\"relational-domain-value\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 \u03b1\u03be\u03af\u03b1 \u03b5\u03bd\u03cc\u03c2 domain \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c7\u03b5\u03c3\u03b9\u03b1\u03ba\u03ae<\/h2>\n<p>\u03a4\u03bf \u03c0\u03b9\u03bf \u03b5\u03bd\u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b9\u03b1 \u03bb\u03af\u03c3\u03c4\u03b1 \u00ab\u03ba\u03b1\u03bb\u03ce\u03bd\u00bb \u03ba\u03b1\u03b9 \u00ab\u03ba\u03b1\u03ba\u03ce\u03bd\u00bb \u03c0\u03b7\u03b3\u03ce\u03bd. \u0395\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03b1\u03be\u03af\u03b1 \u03bc\u03b9\u03b1\u03c2 \u03c0\u03b7\u03b3\u03ae\u03c2 \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c4\u03b9 \u03c4\u03b7 \u03c3\u03c5\u03bd\u03bf\u03b4\u03b5\u03cd\u03b5\u03b9. \u03a3\u03c4\u03bf additive \u03bc\u03ad\u03c1\u03bf\u03c2, domains \u03cc\u03c0\u03c9\u03c2 GitHub, arXiv, PubMed Central, FreeLaw \u03ba\u03b1\u03b9 Stack Exchange \u03b5\u03af\u03c7\u03b1\u03bd \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf\u03c5\u03c2 \u03c3\u03c5\u03bd\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03c6\u03b1\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03bd \u03bf\u03b9 \u03c0\u03b9\u03bf \u03b5\u03c5\u03bd\u03bf\u03ca\u03ba\u03bf\u03af contributors \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7\u03c2 validation loss.<\/p>\n<p>\u03a3\u03c4\u03bf sparse second-order \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd 51 \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b1\u03b9 12 \u03b8\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03b9\u03b4\u03c1\u03ac\u03c3\u03b5\u03b9\u03c2. \u0397 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b4\u03bf\u03bc\u03ae \u03c3\u03c5\u03b3\u03ba\u03b5\u03bd\u03c4\u03c1\u03ce\u03b8\u03b7\u03ba\u03b5 \u03b3\u03cd\u03c1\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf Pile-CC, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae web-derived text. \u039f\u03b9 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03b9\u03b4\u03c1\u03ac\u03c3\u03b5\u03b9\u03c2 \u03c4\u03bf\u03c5 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03bc\u03b5 arXiv (-2,62), PubMed Central (-2,36), FreeLaw (-2,31), GitHub (-2,24), USPTO Backgrounds (-1,83), Stack Exchange (-1,78) \u03ba\u03b1\u03b9 PubMed Abstracts (-1,78).<\/p>\n<p>\u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u00ab\u03c4\u03bf web \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03ac\u03bd\u03c4\u03b1 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf\u00bb. \u03a3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf objective, \u03b7 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf\u03c5 Pile-CC \u03ae\u03c4\u03b1\u03bd \u03ba\u03c5\u03c1\u03af\u03c9\u03c2 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03c9\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae. \u0388\u03bd\u03b1 technical domain \u03c0\u03bf\u03c5 \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b1\u03b4\u03cd\u03bd\u03b1\u03bc\u03bf \u03c3\u03c4\u03bf additive view \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03cc\u03c4\u03b1\u03bd \u03c3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03b5\u03c5\u03c1\u03cd\u03c4\u03b5\u03c1\u03bf web text. \u0391\u03bd\u03c4\u03af\u03c3\u03c4\u03c1\u03bf\u03c6\u03b1, \u03ad\u03bd\u03b1 domain \u03c0\u03bf\u03c5 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03ba\u03b1\u03bb\u03cc \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b2\u03ad\u03b2\u03b1\u03b9\u03bf \u03cc\u03c4\u03b9 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03cc\u03c4\u03b1\u03bd \u03bc\u03c0\u03b1\u03af\u03bd\u03b5\u03b9 \u03c3\u03b5 \u03c3\u03cd\u03bd\u03b8\u03b5\u03c3\u03b7.<\/p>\n<p>\u039f\u03b9 \u03c4\u03c1\u03b5\u03b9\u03c2 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b8\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03b9\u03b4\u03c1\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 DM Mathematics \u03ba\u03b1\u03b9 PubMed Abstracts (+1,62), NIH ExPorter \u03ba\u03b1\u03b9 GitHub (+1,45), \u03ba\u03b1\u03b8\u03ce\u03c2 \u03ba\u03b1\u03b9 PubMed Central \u03ba\u03b1\u03b9 DM Mathematics (+1,11). \u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03b5\u03b9 redundancy \u03ae competition \u03c9\u03c2 \u03c0\u03b9\u03b8\u03b1\u03bd\u03ae \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03af\u03b1 \u03ba\u03ac\u03c4\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf target objective. \u03a0\u03c1\u03cc\u03ba\u03b5\u03b9\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7 \u03c4\u03b7\u03c2 fitted surface, \u03cc\u03c7\u03b9 \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03b2\u03b5\u03b2\u03b1\u03b9\u03c9\u03bc\u03ad\u03bd\u03bf \u03b1\u03b9\u03c4\u03b9\u03ce\u03b4\u03b7 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc.<\/p>\n<aside class=\"td-article-note\"><strong>Practical filter:<\/strong> \u03bc\u03b7\u03bd \u03b1\u03b3\u03bf\u03c1\u03ac\u03b6\u03b5\u03c4\u03b5 \u03ae \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af\u03c4\u03b5 datasets \u03bc\u03cc\u03bd\u03bf \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03b1\u03c5\u03c4\u03cc\u03bd\u03bf\u03bc\u03bf quality score. \u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 provenance, \u03ac\u03b4\u03b5\u03b9\u03b1, \u03ba\u03ac\u03bb\u03c5\u03c8\u03b7 \u03c4\u03bf\u03c5 target task, \u03b5\u03c0\u03b9\u03ba\u03ac\u03bb\u03c5\u03c8\u03b7 \u03bc\u03b5 \u03c4\u03b9\u03c2 \u03c5\u03c0\u03cc\u03bb\u03bf\u03b9\u03c0\u03b5\u03c2 \u03c0\u03b7\u03b3\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bf\u03bb\u03ae \u03c4\u03b7\u03c2 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7\u03c2 \u03cc\u03c4\u03b1\u03bd \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03b7 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03ae \u03c3\u03cd\u03bd\u03b8\u03b5\u03c3\u03b7.<\/aside>\n<h2 id=\"rank-preservation\">\u03a0\u03ce\u03c2 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b7 \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03b1\u03c0\u03cc 1M \u03ad\u03c9\u03c2 1B<\/h2>\n<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03ba\u03c1\u03b9\u03bd\u03b5 \u03c4\u03bf first-order Scheff\u00e9, \u03c4\u03bf sparse second-order Scheff\u00e9 \u03ba\u03b1\u03b9 \u03c4\u03bf LightGBM \u03bc\u03b5 \u03b4\u03cd\u03bf metrics: Spearman rank correlation \u03ba\u03b1\u03b9 pairwise ranking accuracy. \u038c\u03bb\u03b1 \u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 fitted \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03b1 512 proxy mixtures \u03c4\u03bf\u03c5 1M scale. \u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 256 held-out mixtures \u03c3\u03c4\u03b1 1M \u03ba\u03b1\u03b9 60M, \u03b1\u03bb\u03bb\u03ac \u03bc\u03cc\u03bd\u03bf 64 \u03c3\u03c4\u03bf 1B, \u03bc\u03b5 2.000 bootstrap replicates \u03b3\u03b9\u03b1 confidence intervals.<\/p>\n<p>\u03a4\u03bf first-order model \u03ba\u03c1\u03ac\u03c4\u03b7\u03c3\u03b5 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 ranking signal: \u03b7 Spearman correlation \u03ba\u03c5\u03bc\u03ac\u03bd\u03b8\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc 0,879 \u03ad\u03c9\u03c2 0,902 \u03ba\u03b1\u03b9 \u03c4\u03bf PRA \u03ad\u03bc\u03b5\u03b9\u03bd\u03b5 \u03c0\u03ac\u03bd\u03c9 \u03b1\u03c0\u03cc 0,85. \u039c\u03b5 \u03c4\u03bf\u03c5\u03c2 sparse pairwise \u03cc\u03c1\u03bf\u03c5\u03c2, \u03c3\u03c4\u03bf 1M \u03c4\u03bf rho \u03b1\u03bd\u03ad\u03b2\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc 0,902 \u03c3\u03b5 0,937 \u03ba\u03b1\u03b9 \u03c4\u03bf PRA \u03b1\u03c0\u03cc 0,867 \u03c3\u03b5 0,894. \u03a3\u03c4\u03bf 60M, \u03bf\u03b9 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03bc\u03b5\u03c4\u03b1\u03ba\u03b9\u03bd\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd \u03b1\u03c0\u03cc 0,892 \u03c3\u03b5 0,939 \u03ba\u03b1\u03b9 \u03b1\u03c0\u03cc 0,859 \u03c3\u03b5 0,896.<\/p>\n<p>\u03a3\u03c4\u03bf 1B, \u03c4\u03bf sparse second-order model \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 rho 0,975 \u03ba\u03b1\u03b9 PRA 0,937, \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 0,879 \u03ba\u03b1\u03b9 0,864 \u03b3\u03b9\u03b1 \u03c4\u03bf first-order. \u03a4\u03bf LightGBM \u03b5\u03af\u03c7\u03b5 0,962 \u03ba\u03b1\u03b9 0,927. \u0397 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ae \u03c5\u03c0\u03b5\u03c1\u03bf\u03c7\u03ae \u03c4\u03bf\u03c5 Scheff\u00e9 \u03c3\u03c4\u03bf 1B \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b5\u03af \u03c9\u03c2 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ae \u03bd\u03af\u03ba\u03b7: \u03c4\u03b1 confidence intervals \u03b5\u03c0\u03b9\u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03bf\u03bd\u03c4\u03b1\u03bd \u03ba\u03b1\u03b9 \u03c4\u03bf sample \u03b5\u03af\u03c7\u03b5 \u03bc\u03cc\u03bb\u03b9\u03c2 64 mixtures. \u03a4\u03bf \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03b9\u03c9\u03bc\u03ad\u03bd\u03bf \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 comparable performance \u03bc\u03b5 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ac \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03c5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1.<\/p>\n<p>\u03a3\u03c4\u03b9\u03c2 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b5\u03c2, \u03c4\u03bf LightGBM \u03c0\u03b1\u03c1\u03ad\u03bc\u03b5\u03b9\u03bd\u03b5 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf: rho 0,990 \u03ba\u03b1\u03b9 PRA 0,959 \u03c3\u03c4\u03bf 1M, \u03ba\u03b1\u03b8\u03ce\u03c2 \u03ba\u03b1\u03b9 0,986 \u03ba\u03b1\u03b9 0,951 \u03c3\u03c4\u03bf 60M. \u03a4\u03bf paper \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c1\u03c1\u03af\u03c0\u03c4\u03b5\u03b9 \u03c4\u03bf\u03c5\u03c2 flexible predictors. \u03a0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03b5\u03b9 \u03ad\u03bd\u03b1\u03bd \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03c3\u03c5\u03bc\u03b2\u03b9\u03b2\u03b1\u03c3\u03bc\u03cc: sparse response surface \u03c0\u03bf\u03c5 \u03ba\u03c1\u03b1\u03c4\u03ac \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03b1 \u03b5\u03be\u03b7\u03b3\u03b5\u03af \u03c0\u03bf\u03b9\u03b1 additive \u03ba\u03b1\u03b9 interaction effects \u03c4\u03b7 \u03b4\u03b9\u03b1\u03bc\u03bf\u03c1\u03c6\u03ce\u03bd\u03bf\u03c5\u03bd. \u0391\u03c5\u03c4\u03ae \u03b7 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03ad\u03c7\u03b5\u03b9 \u03b1\u03be\u03af\u03b1 \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 <a href=\"https:\/\/twodots.gr\/ai-benchmark-harness-allazei-nikiti\/\">AI benchmark \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03b1\u03bd\u03ac\u03bb\u03bf\u03b3\u03b1 \u03bc\u03b5 \u03c4\u03bf evaluation harness<\/a>.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-kicker\">\u039a\u03b1\u03bd\u03cc\u03bd\u03b1\u03c2 scale-up<\/p>\n<p class=\"td-decision-title\">\u039c\u03b7\u03bd \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03c4\u03b5 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf training run \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03bc\u03cc\u03bd\u03bf point estimate<\/p>\n<p>\u0391\u03c0\u03b1\u03b9\u03c4\u03ae\u03c3\u03c4\u03b5 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03b7\u03c2 \u03ba\u03bf\u03c1\u03c5\u03c6\u03b1\u03af\u03b1\u03c2 \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7\u03c2 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac model forms, confidence intervals, sensitivity analyses \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b7\u03c0\u03c4\u03b9\u03ba\u03ac proxy runs. \u0391\u03bd \u03bc\u03b9\u03ba\u03c1\u03ad\u03c2 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 \u03c3\u03c4\u03bf objective \u03ae \u03c3\u03c4\u03b7 \u03c3\u03cd\u03bd\u03b8\u03b5\u03c3\u03b7 \u03b1\u03bd\u03b1\u03c4\u03c1\u03ad\u03c0\u03bf\u03c5\u03bd \u03c4\u03b1 \u03c0\u03c1\u03ce\u03c4\u03b1 mixtures, \u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b5\u03c0\u03ad\u03bd\u03b4\u03c5\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03ad\u03bf\u03b9 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03b9\u03b1\u03ba\u03bf\u03af \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03bf\u03af \u03c0\u03cc\u03bd\u03c4\u03bf\u03b9 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b2\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc scale-up.<\/p>\n<\/div>\n<h2 id=\"i-optimal-design\">\u03a0\u03bf\u03b9\u03b1 proxy runs \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03c4\u03bf\u03cd\u03bd<\/h2>\n<p>\u03a4\u03bf \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03b9\u03c3\u03cc \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf \u03b5\u03c1\u03ce\u03c4\u03b7\u03bc\u03b1. \u0391\u03bd\u03c4\u03af \u03bd\u03b1 \u03c1\u03c9\u03c4\u03ac \u03bc\u03cc\u03bd\u03bf \u00ab\u03c0\u03bf\u03b9\u03bf\u03c2 predictor \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c3\u03c4\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1;\u00bb, \u03c1\u03c9\u03c4\u03ac \u00ab\u03c0\u03bf\u03b9\u03b5\u03c2 mixture configurations \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03c4\u03bf\u03cd\u03bd;\u00bb. \u0397 I-optimality \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03c4\u03b1\u03b9 \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03c3\u03c4\u03bf\u03c7\u03b5\u03cd\u03b5\u03b9 \u03c3\u03c4\u03b7 \u03c7\u03b1\u03bc\u03b7\u03bb\u03ae \u03bc\u03ad\u03c3\u03b7 prediction variance \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03b7\u03bd \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ae. \u0397 D-optimality \u03b5\u03c3\u03c4\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03c3\u03c4\u03b7\u03bd \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03c4\u03c9\u03bd \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd, \u03b5\u03bd\u03ce \u03b5\u03b4\u03ce \u03c4\u03bf \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03cc \u03b6\u03b7\u03c4\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 unseen mixtures.<\/p>\n<p>\u0395\u03c0\u03b5\u03b9\u03b4\u03ae \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b3\u03bd\u03c9\u03c3\u03c4\u03cc \u03b1\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03bc\u03b9\u03b1 additive surface \u03ae \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 interactions, \u03c4\u03bf criterion \u03b5\u03af\u03bd\u03b1\u03b9 model-robust: \u03b4\u03af\u03bd\u03b5\u03b9 \u03af\u03c3\u03bf \u03b2\u03ac\u03c1\u03bf\u03c2 \u03c3\u03c4\u03bf first-order \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf full second-order Scheff\u00e9 model. \u039f\u03b9 designs \u03ba\u03b1\u03c4\u03b1\u03c3\u03ba\u03b5\u03c5\u03ac\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03bc\u03b5 simulated annealing \u03b3\u03b9\u03b1 \u03bc\u03b5\u03b3\u03ad\u03b8\u03b7 \u03b1\u03c0\u03cc 160 \u03ad\u03c9\u03c2 512 runs.<\/p>\n<p>\u0397 \u03b3\u03b5\u03c9\u03bc\u03b5\u03c4\u03c1\u03af\u03b1 \u03c4\u03bf\u03c5\u03c2 \u03b4\u03b9\u03ad\u03c6\u03b5\u03c1\u03b5 \u03b1\u03c0\u03cc \u03c4\u03bf random Dirichlet sampling \u03c4\u03bf\u03c5 RegMix. \u0391\u03bd\u03c4\u03af \u03b3\u03b9\u03b1 \u03c3\u03c5\u03bd\u03b5\u03c7\u03ce\u03c2 \u03b1\u03c0\u03bb\u03c9\u03bc\u03ad\u03bd\u03b5\u03c2 \u03bc\u03b9\u03ba\u03c1\u03ad\u03c2 \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b5\u03c2, \u03bf\u03b9 optimal designs \u03c3\u03c5\u03b3\u03ba\u03ad\u03bd\u03c4\u03c1\u03c9\u03c3\u03b1\u03bd \u03c0\u03bf\u03bb\u03bb\u03ac \u03c3\u03b7\u03bc\u03b5\u03af\u03b1 \u03c3\u03c4\u03b1 0, 0,5 \u03ba\u03b1\u03b9 1. \u039f\u03b9 \u03ba\u03bf\u03c1\u03c5\u03c6\u03ad\u03c2 \u03c4\u03bf\u03c5 simplex \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03bf\u03cd\u03bd \u03b3\u03b9\u03b1 first-order effects, \u03b5\u03bd\u03ce \u03c4\u03b1 midpoints \u03c4\u03c9\u03bd \u03b1\u03ba\u03bc\u03ce\u03bd \u03b2\u03bf\u03b7\u03b8\u03bf\u03cd\u03bd \u03c3\u03c4\u03b7\u03bd \u03b5\u03ba\u03c4\u03af\u03bc\u03b7\u03c3\u03b7 pairwise interactions. \u0391\u03c5\u03c4\u03ac \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03bf\u03c4\u03b5\u03b9\u03bd\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03bd\u03c4\u03b1\u03b3\u03ad\u03c2 pretraining, \u03b1\u03bb\u03bb\u03ac \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03b9\u03b1\u03ba\u03ac experimental points.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2, \u03b7 \u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03b8\u03c5\u03bc\u03af\u03b6\u03b5\u03b9 \u03c3\u03c9\u03c3\u03c4\u03ac \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03ad\u03bd\u03bf pilot. \u0394\u03b5\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c0\u03bf\u03bb\u03bb\u03ac runs\u00b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 runs \u03c0\u03bf\u03c5 \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c5\u03c0\u03bf\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2. \u03a4\u03bf \u03af\u03b4\u03b9\u03bf \u03b9\u03c3\u03c7\u03cd\u03b5\u03b9 \u03c3\u03b5 <a href=\"https:\/\/twodots.gr\/dpo-preference-data-audit-llm\/\">\u03ad\u03bb\u03b5\u03b3\u03c7\u03bf preference data \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc DPO<\/a> \u03ae \u03c3\u03b5 domain fine-tuning: \u03b7 \u03c0\u03bf\u03b9\u03ba\u03b9\u03bb\u03af\u03b1 \u03c0\u03bf\u03c5 \u03bc\u03b5\u03c4\u03c1\u03ac \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03ba\u03b5\u03af\u03bd\u03b7 \u03c0\u03bf\u03c5 \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7.<\/p>\n<h2 id=\"simulation-saving\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 25% \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b1 runs<\/h2>\n<p>\u03a3\u03c4\u03b7\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7, 1.000 random Dirichlet designs \u03c4\u03c9\u03bd 512 runs \u03b5\u03af\u03c7\u03b1\u03bd relative I-efficiency \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 0,07 \u03ad\u03c9\u03c2 0,08 \u03c3\u03b5 \u03c3\u03c7\u03ad\u03c3\u03b7 \u03bc\u03b5 \u03c4\u03bf\u03bd model-robust optimal design \u03ba\u03ac\u03c4\u03c9 \u03b1\u03c0\u03cc \u03c4\u03b7 sparse second-order \u03b2\u03ac\u03c3\u03b7. \u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 \u03bc\u03ad\u03c3\u03b7 prediction variance \u03b3\u03b9\u03b1 \u03c4\u03b1 \u03c4\u03c5\u03c7\u03b1\u03af\u03b1 designs \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bd\u03b1 \u03b5\u03be\u03b5\u03c4\u03ac\u03c3\u03bf\u03c5\u03bd \u03b1\u03bd \u03c4\u03bf \u03c0\u03bb\u03b5\u03bf\u03bd\u03ad\u03ba\u03c4\u03b7\u03bc\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03c1\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 ranking, \u03bf\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b1\u03bd \u03c9\u03c2 data-generating surface \u03c4\u03bf sparse Scheff\u00e9 model \u03c0\u03bf\u03c5 fitted \u03c3\u03c4\u03b1 \u03c0\u03bb\u03ae\u03c1\u03b7 RegMix data, \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c3\u03b1\u03bd Gaussian noise \u03b1\u03c0\u03cc \u03c4\u03b1 residuals \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03ad\u03bb\u03b1\u03b2\u03b1\u03bd \u03c4\u03b7 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 1.000 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 design size. \u03a0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 \u03c3\u03c4\u03b1 350 proxy runs, \u03c4\u03b1 model-robust I-optimal designs \u03ad\u03c6\u03c4\u03b1\u03c3\u03b1\u03bd \u03ae \u03be\u03b5\u03c0\u03ad\u03c1\u03b1\u03c3\u03b1\u03bd \u03c4\u03bf reference \u03c4\u03c9\u03bd 512 RegMix runs. \u039c\u03b5 \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7\u03bd \u03ad\u03bd\u03bd\u03bf\u03b9\u03b1 \u03b1\u03c6\u03b1\u03af\u03c1\u03b5\u03c3\u03b1\u03bd \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 \u03c4\u03bf 25% \u03c4\u03c9\u03bd runs \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03ce\u03bd\u03c4\u03b1\u03c2 \u03c4\u03b7\u03bd \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7 \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7.<\/p>\n<p>\u03a4\u03bf \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03cc \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03b1\u03c0\u03b5\u03af \u03c3\u03b5 marketing promise \u03ae fixed compute budget. \u03a4\u03bf simulation \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 responses \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 fitted surface \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03c9\u03c2 \u03b2\u03ac\u03c3\u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2, \u03bc\u03b5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 noise assumption. \u0391\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af \u03ad\u03bd\u03b4\u03b5\u03b9\u03be\u03b7 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf case study, \u03cc\u03c7\u03b9 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b7 \u03b5\u03c0\u03b9\u03b2\u03b5\u03b2\u03b1\u03af\u03c9\u03c3\u03b7 \u03c3\u03b5 \u03bd\u03ad\u03bf large-scale training campaign.<\/p>\n<p>\u0397 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b1\u03c1\u03c7\u03ae \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03bf\u03b9\u03bf\u03c4\u03b9\u03ba\u03ae: \u03cc\u03c4\u03b1\u03bd \u03ba\u03ac\u03b8\u03b5 proxy run \u03ba\u03b1\u03c4\u03b1\u03bd\u03b1\u03bb\u03ce\u03bd\u03b5\u03b9 tokens, \u03c7\u03c1\u03cc\u03bd\u03bf \u03ba\u03b1\u03b9 compute, \u03b7 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03c4\u03c9\u03bd runs \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03b7\u03b8\u03b5\u03af \u03b1\u03bd\u03c4\u03af \u03bd\u03b1 \u03b1\u03c6\u03ae\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c3\u03c4\u03b7\u03bd \u03c4\u03c5\u03c7\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1. \u03a4\u03bf \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03cc \u03bd\u03cc\u03b7\u03bc\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03ac\u03c4\u03b1\u03b9 \u03b1\u03bd\u03ac project, \u03cc\u03c0\u03c9\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 <a href=\"https:\/\/twodots.gr\/ai-roi-axia-ergasias\/\">AI ROI \u03b1\u03c0\u03cc \u03c4\u03b1 tokens \u03ad\u03c9\u03c2 \u03c4\u03b7\u03bd \u03b1\u03be\u03af\u03b1 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2<\/a>.<\/p>\n<h2 id=\"business-translation\">\u0397 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03bc\u03b5\u03c4\u03ac\u03c6\u03c1\u03b1\u03c3\u03b7 \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf pretraining<\/h2>\n<p>\u0397 \u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03b5\u03c0\u03b5\u03ba\u03c4\u03b5\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 AI \u03c0\u03c1\u03cc\u03b3\u03c1\u03b1\u03bc\u03bc\u03b1 \u03cc\u03c0\u03bf\u03c5 \u03ad\u03bd\u03b1\u03c2 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c2 \u03c0\u03cc\u03c1\u03bf\u03c2 \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03c0\u03b7\u03b3\u03ad\u03c2. Multimodal \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03c4\u03b1\u03bd\u03ad\u03bc\u03bf\u03c5\u03bd budget \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1, \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf, video \u03ba\u03b1\u03b9 audio. Multilingual models \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03bf\u03c5\u03bd tokens \u03b1\u03bd\u03ac \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1 \u03ae \u03b1\u03b3\u03bf\u03c1\u03ac. Instruction tuning \u03ba\u03b1\u03c4\u03b1\u03bd\u03ad\u03bc\u03b5\u03b9 \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03b1\u03bd\u03ac task family. \u0388\u03bd\u03b1 \u03b5\u03c4\u03b1\u03b9\u03c1\u03b9\u03ba\u03cc retrieval \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03b1\u03c0\u03bf\u03c6\u03b1\u03c3\u03af\u03b6\u03b5\u03b9 \u03c0\u03cc\u03c3\u03bf \u03b2\u03ac\u03c1\u03bf\u03c2 \u03b4\u03af\u03bd\u03b5\u03b9 \u03c3\u03b5 policies, tickets, product data \u03ba\u03b1\u03b9 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03af\u03c9\u03c3\u03b7.<\/p>\n<p>\u03a3\u03b5 \u03b1\u03c5\u03c4\u03ad\u03c2 \u03c4\u03b9\u03c2 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ad\u03c2, \u03b7 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ae \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03bc\u03bf\u03bd\u03ac\u03b4\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u00ab\u03ad\u03bd\u03b1 dataset\u00bb, \u03b1\u03bb\u03bb\u03ac \u03bc\u03b9\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03c4\u03bf\u03c5 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03bf\u03cd learning \u03ae evaluation budget. \u0393\u03b9\u2019 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/data-silos-audit-trail-smart-manufacturing\/\">audit trail \u03c4\u03c9\u03bd \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd<\/a> \u03ba\u03b1\u03b9 \u03c4\u03bf lineage \u03ba\u03ac\u03b8\u03b5 run \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03b1\u03c1\u03b1\u03af\u03c4\u03b7\u03c4\u03b1: \u03c7\u03c9\u03c1\u03af\u03c2 \u03b1\u03ba\u03c1\u03b9\u03b2\u03b5\u03af\u03c2 \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b5\u03c2, token counts \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf filtering, seed, model configuration \u03ba\u03b1\u03b9 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2, \u03c4\u03b1 interactions \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03af\u03b4\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7 \u03c3\u03cd\u03bd\u03b8\u03b5\u03c3\u03b7 \u03b5\u03bd\u03ce \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03b1\u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03b9\u03b5\u03c2 \u03c4\u03bf\u03c5 pipeline.<\/p>\n<p>\u0397 \u03c3\u03c7\u03b5\u03c3\u03b9\u03b1\u03ba\u03ae \u03b1\u03be\u03af\u03b1 \u03c4\u03c9\u03bd \u03c0\u03b7\u03b3\u03ce\u03bd \u03b2\u03bf\u03b7\u03b8\u03ac \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03b1\u03c0\u03bf\u03c6\u03c5\u03b3\u03ae model collapse. \u0397 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ae\u03ba\u03b7 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf\u03c5 \u03cc\u03b3\u03ba\u03bf\u03c5 synthetic data \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b7 \u03b1\u03c0\u03cc \u03c4\u03bf real-data mix, \u03c4\u03bf filtering \u03ba\u03b1\u03b9 \u03c4\u03bf objective. \u038c\u03c0\u03c9\u03c2 \u03b5\u03be\u03b7\u03b3\u03b5\u03af \u03b7 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/model-collapse-ai-synthetic-data\/\">model collapse \u03cc\u03c4\u03b1\u03bd \u03b7 AI \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7 \u03b4\u03b9\u03ba\u03ae \u03c4\u03b7\u03c2 \u03b7\u03c7\u03ce<\/a>, \u03b7 \u03c0\u03c1\u03bf\u03ad\u03bb\u03b5\u03c5\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03b7 \u03b9\u03c3\u03bf\u03c1\u03c1\u03bf\u03c0\u03af\u03b1 \u03c4\u03c9\u03bd \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03bf\u03c1\u03b1\u03c4\u03ad\u03c2.<\/p>\n<p>\u0393\u03b9\u03b1 \u03c4\u03b9\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2, \u03bf \u03c3\u03c4\u03cc\u03c7\u03bf\u03c2 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03bd\u03c4\u03b9\u03b3\u03c1\u03ac\u03c8\u03bf\u03c5\u03bd \u03c4\u03bf\u03bd RegMix. \u0395\u03af\u03bd\u03b1\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03c0\u03b5\u03b9\u03b8\u03b1\u03c1\u03c7\u03af\u03b1: \u03c3\u03b1\u03c6\u03ad\u03c2 budget, \u03b5\u03c6\u03b9\u03ba\u03c4\u03ae \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ae, \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03b9\u03c9\u03bc\u03ad\u03bd\u03b5\u03c2 \u03c0\u03b7\u03b3\u03ad\u03c2, \u03c0\u03c1\u03bf\u03b5\u03b3\u03b3\u03b5\u03b3\u03c1\u03b1\u03bc\u03bc\u03ad\u03bd\u03bf response metric, \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03bf\u03af \u03c0\u03cc\u03bd\u03c4\u03bf\u03b9 \u03c0\u03bf\u03c5 \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c5\u03c0\u03bf\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 scale-up \u03c0\u03bf\u03c5 \u03b2\u03b1\u03c3\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 ranking stability \u03b1\u03bd\u03c4\u03af \u03b3\u03b9\u03b1 \u03ad\u03bd\u03b1\u03bd \u03b5\u03bd\u03c4\u03c5\u03c0\u03c9\u03c3\u03b9\u03b1\u03ba\u03cc \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc.<\/p>\n<h2 id=\"praktiko-protokollo\">\u0395\u03c0\u03c4\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03b5\u03bb\u03ad\u03b3\u03be\u03b9\u03bc\u03bf data-mixing experiment<\/h2>\n<p>\u03a4\u03bf \u03b1\u03ba\u03cc\u03bb\u03bf\u03c5\u03b8\u03bf \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf \u03bc\u03b5\u03c4\u03b1\u03c6\u03c1\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b7 \u03bc\u03b5\u03b8\u03bf\u03b4\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03b9\u03b4\u03ad\u03b1 \u03c3\u03b5 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03ae\u03c8\u03b9\u03bc\u03b7 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03b3\u03b9\u03b1 AI, data \u03ba\u03b1\u03b9 product \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2. \u0394\u03b5\u03bd \u03c5\u03c0\u03bf\u03b8\u03ad\u03c4\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf Scheff\u00e9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03ac\u03bd\u03c4\u03b1 \u03b7 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae\u00b7 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03cc\u03bc\u03c9\u03c2 \u03ba\u03ac\u03b8\u03b5 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03bb\u03ad\u03b3\u03be\u03b9\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c4\u03b7\u03bd \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 scale-up.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u0391\u03c0\u03cc \u03c4\u03bf corpus inventory \u03c3\u03b5 \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03b9\u03c9\u03bc\u03ad\u03bd\u03b7 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 scale-up<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">Step 1<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03c4\u03bf \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc budget \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc objective<\/strong>\n<p>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 tokens, compute, \u03c7\u03c1\u03cc\u03bd\u03bf \u03ba\u03b1\u03b9 response metric. \u0394\u03b9\u03b1\u03c7\u03c9\u03c1\u03af\u03c3\u03c4\u03b5 language-model loss, domain-task quality, safety, \u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03ae \u03ba\u03ac\u03bb\u03c5\u03c8\u03b7 \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b1\u03bd\u03c4\u03af \u03bd\u03b1 \u03c4\u03b1 \u03c3\u03c5\u03bc\u03c0\u03c5\u03ba\u03bd\u03ce\u03bd\u03b5\u03c4\u03b5 \u03c3\u03b5 \u03ad\u03bd\u03b1\u03bd \u03b1\u03c3\u03b1\u03c6\u03ae \u03c3\u03c4\u03cc\u03c7\u03bf.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 2<\/span><strong>\u03a7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03ae\u03c3\u03c4\u03b5 domains, provenance \u03ba\u03b1\u03b9 \u03ac\u03b4\u03b5\u03b9\u03b5\u03c2<\/strong>\n<p>\u0393\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03c0\u03b7\u03b3\u03ae \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 \u03c0\u03c1\u03bf\u03ad\u03bb\u03b5\u03c5\u03c3\u03b7, \u03ac\u03b4\u03b5\u03b9\u03b1, filtering, deduplication, \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03bf \u03cc\u03b3\u03ba\u03bf, preprocessing cost \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u03c7\u03c1\u03ae\u03c3\u03b7\u03c2. \u0391\u03c5\u03c4\u03ac \u03ba\u03b1\u03b8\u03bf\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c0\u03bf\u03b9\u03b1 \u03c3\u03b7\u03bc\u03b5\u03af\u03b1 \u03c4\u03bf\u03c5 simplex \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03ac\u03b3\u03bc\u03b1\u03c4\u03b9 \u03b5\u03c6\u03b9\u03ba\u03c4\u03ac.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 3<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 bounds \u03ba\u03b1\u03b9 \u03c5\u03c0\u03bf\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7\u03c2<\/strong>\n<p>\u0391\u03c0\u03bf\u03c6\u03b1\u03c3\u03af\u03c3\u03c4\u03b5 \u03b5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03bc\u03ad\u03b3\u03b9\u03c3\u03c4\u03b1 token shares, \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u03ba\u03b1\u03b9 \u03b6\u03b5\u03cd\u03b3\u03b7 \u03c0\u03bf\u03c5 \u03b1\u03be\u03af\u03b6\u03bf\u03c5\u03bd \u03b5\u03b9\u03b4\u03b9\u03ba\u03cc \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf. \u039c\u03b7\u03bd \u03c5\u03c0\u03bf\u03b8\u03ad\u03c4\u03b5\u03c4\u03b5 \u03cc\u03c4\u03b9 \u03c4\u03bf quality score \u03ba\u03ac\u03b8\u03b5 domain \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03bc\u03ad\u03c3\u03b1 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 mixture.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 4<\/span><strong>\u0395\u03c0\u03b9\u03bb\u03ad\u03be\u03c4\u03b5 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03b9\u03b1\u03ba\u03ac proxy mixtures<\/strong>\n<p>\u03a3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 random sampling \u03bc\u03b5 design-aware \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03c0\u03bf\u03c5 \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 vertices, midpoints \u03ba\u03b1\u03b9 \u03c1\u03b5\u03b1\u03bb\u03b9\u03c3\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03bd\u03b4\u03b9\u03ac\u03bc\u03b5\u03c3\u03b5\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2. \u039a\u03c1\u03b1\u03c4\u03ae\u03c3\u03c4\u03b5 holdout mixtures \u03b3\u03b9\u03b1 \u03c4\u03af\u03bc\u03b9\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 5<\/span><strong>Version\u03ac\u03c1\u03b5\u03c4\u03b5 \u03ba\u03ac\u03b8\u03b5 run \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03ae \u03c4\u03bf\u03c5<\/strong>\n<p>\u0391\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03cd\u03c3\u03c4\u03b5 \u03b1\u03ba\u03c1\u03b9\u03b2\u03b5\u03af\u03c2 \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b5\u03c2, post-filter token counts, seeds, checkpoints, code, hardware assumptions \u03ba\u03b1\u03b9 evaluation version. \u03a7\u03c9\u03c1\u03af\u03c2 lineage, \u03b7 fitted surface \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03bb\u03ad\u03b3\u03be\u03b9\u03bc\u03b7.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 6<\/span><strong>\u0394\u03b9\u03b1\u03b2\u03ac\u03c3\u03c4\u03b5 main effects, interactions \u03ba\u03b1\u03b9 uncertainty \u03bc\u03b1\u03b6\u03af<\/strong>\n<p>\u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 ranking metrics, confidence intervals, stability \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac model forms \u03ba\u03b1\u03b9 targeted reruns \u03c4\u03c9\u03bd \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03c9\u03bd pairwise effects. \u039c\u03b7\u03bd \u03b2\u03b1\u03c6\u03c4\u03af\u03b6\u03b5\u03c4\u03b5 \u03ad\u03bd\u03b1\u03bd coefficient \u03b1\u03b9\u03c4\u03b9\u03ce\u03b4\u03b7 \u03ba\u03b1\u03bd\u03cc\u03bd\u03b1.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 7<\/span><strong>\u039a\u03bb\u03b9\u03bc\u03b1\u03ba\u03ce\u03c3\u03c4\u03b5 \u03bc\u03cc\u03bd\u03bf \u03bc\u03b5 \u03c0\u03c1\u03bf\u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf gate<\/strong>\n<p>\u0391\u03c0\u03b1\u03b9\u03c4\u03ae\u03c3\u03c4\u03b5 \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03ae rank preservation, \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03c9\u03bd \u03ba\u03bf\u03c1\u03c5\u03c6\u03b1\u03af\u03c9\u03bd mixtures, \u03c3\u03c5\u03bc\u03bc\u03cc\u03c1\u03c6\u03c9\u03c3\u03b7 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf budget. \u0391\u03bd \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03cd\u03b8\u03c1\u03b1\u03c5\u03c3\u03c4\u03bf, \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c3\u03c4\u03b5 informative proxy points \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf training run.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<h2 id=\"periorismoi-governance\">\u03a0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af, data governance \u03ba\u03b1\u03b9 \u03bb\u03ac\u03b8\u03bf\u03c2 objectives<\/h2>\n<p>\u0397 \u03b5\u03bc\u03c0\u03b5\u03b9\u03c1\u03b9\u03ba\u03ae \u03b2\u03ac\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03b4\u03b7\u03bc\u03cc\u03c3\u03b9\u03bf proxy-training dataset. \u0391\u03bd \u03b7 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae response surface \u03ad\u03c7\u03b5\u03b9 higher-order interactions, thresholds \u03ae \u03b9\u03c3\u03c7\u03c5\u03c1\u03ad\u03c2 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ad\u03c2 \u03b1\u03bd\u03c9\u03bc\u03b1\u03bb\u03af\u03b5\u03c2, \u03ad\u03bd\u03b1 sparse quadratic Scheff\u00e9 model \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c5\u03c0\u03b5\u03c1\u03b2\u03bf\u03bb\u03b9\u03ba\u03ac \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc. \u03a4\u03bf design evaluation \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf fitted surface \u03c9\u03c2 mechanism \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae\u03c2 \u03c4\u03c9\u03bd simulated responses, \u03ac\u03c1\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b7 \u03b5\u03c0\u03b9\u03b2\u03b5\u03b2\u03b1\u03af\u03c9\u03c3\u03b7 \u03c3\u03b5 \u03bd\u03ad\u03bf \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7\u03c2 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b1\u03c2.<\/p>\n<p>\u039f\u03b9 optimal designs \u03c5\u03c0\u03bf\u03b8\u03ad\u03c4\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03c4\u03b1 \u03c3\u03b7\u03bc\u03b5\u03af\u03b1 \u03c4\u03bf\u03c5 simplex \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b1 \u03ba\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03b1 runs \u03ad\u03c7\u03bf\u03c5\u03bd \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2. \u03a3\u03c4\u03b7\u03bd \u03c0\u03c1\u03ac\u03be\u03b7, \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b5\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03b4\u03cd\u03bd\u03b1\u03c4\u03b5\u03c2 \u03bb\u03cc\u03b3\u03c9 \u03b4\u03b9\u03b1\u03b8\u03b5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2, licensing, deduplication, filtering, privacy, unequal preprocessing \u03ae compute cost. \u0391\u03c5\u03c4\u03bf\u03af \u03bf\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03bd\u03c3\u03c9\u03bc\u03b1\u03c4\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd feasible region \u03ae \u03c3\u03b5 cost-sensitive \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03cc, \u03cc\u03c7\u03b9 \u03bd\u03b1 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b5\u03ba \u03c4\u03c9\u03bd \u03c5\u03c3\u03c4\u03ad\u03c1\u03c9\u03bd \u03c9\u03c2 compliance checklist.<\/p>\n<p>\u0388\u03bd\u03b1 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf validation loss \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03b1\u03c5\u03c4\u03bf\u03bc\u03ac\u03c4\u03c9\u03c2 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2, \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1 \u03ae \u03ba\u03b1\u03c4\u03b1\u03bb\u03bb\u03b7\u03bb\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5\u03c2 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b5\u03c2. \u0397 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae response variable \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7. \u0391\u03bd \u03c4\u03bf objective \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c4\u03b5\u03bd\u03cc \u03ae \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03bf, \u03ad\u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03bf\u03c4\u03b9\u03ba\u03ac \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03ad\u03bd\u03bf \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03b1\u03c0\u03bb\u03ce\u03c2 \u03bd\u03b1 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03b1 \u03c4\u03bf\u03bd \u03bb\u03ac\u03b8\u03bf\u03c2 \u03c3\u03c4\u03cc\u03c7\u03bf. \u03a4\u03bf NIST AI RMF \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03b5\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7 \u03c3\u03cd\u03bd\u03b4\u03b5\u03c3\u03b7 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 governance, context mapping, measurement \u03ba\u03b1\u03b9 risk management.<\/p>\n<p>\u0397 \u03af\u03b4\u03b9\u03b1 \u03b1\u03c1\u03c7\u03ae \u03b9\u03c3\u03c7\u03cd\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 explainability \u03ae model-selection metric. \u0388\u03bd\u03b1 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc score \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03b1\u03bd \u03b4\u03b5\u03bd \u03be\u03ad\u03c1\u03bf\u03c5\u03bc\u03b5 \u03c4\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ac, \u03c3\u03b5 \u03c0\u03bf\u03b9\u03bf\u03bd \u03c0\u03bb\u03b7\u03b8\u03c5\u03c3\u03bc\u03cc \u03ba\u03b1\u03b9 \u03bc\u03b5 \u03c0\u03bf\u03b9\u03b1 \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1. \u0397 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae \u03b1\u03c5\u03c4\u03ae \u03b1\u03bd\u03b1\u03bb\u03cd\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf \u03ac\u03c1\u03b8\u03c1\u03bf \u03b3\u03b9\u03b1 \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/explainable-ai-ypsilo-auc-axiopistes-exigiseis\/\">\u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf \u03c5\u03c8\u03b7\u03bb\u03cc AUC \u03b4\u03b5\u03bd \u03b5\u03b3\u03b3\u03c5\u03ac\u03c4\u03b1\u03b9 \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03b5\u03c2 \u03b5\u03be\u03b7\u03b3\u03ae\u03c3\u03b5\u03b9\u03c2<\/a>.<\/p>\n<h2 id=\"stratigiki-dedomenon\">\u0391\u03c0\u03cc \u03c4\u03b7 \u03c3\u03c5\u03bd\u03c4\u03b1\u03b3\u03ae \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03c3\u03c4\u03b7 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae \u03bc\u03ac\u03b8\u03b7\u03c3\u03b7\u03c2<\/h2>\n<p>\u03a4\u03bf \u03ba\u03b5\u03bd\u03c4\u03c1\u03b9\u03ba\u03cc \u03bc\u03ae\u03bd\u03c5\u03bc\u03b1 \u03c4\u03b7\u03c2 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03c1\u03b3\u03b1\u03bd\u03c9\u03c4\u03b9\u03ba\u03cc: \u03b7 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03bf\u03c5 \u03b1\u03c0\u03bf\u03c6\u03b1\u03c3\u03af\u03b6\u03b5\u03b9 \u03c0\u03bf\u03b9\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b8\u03b1 \u03b4\u03bf\u03ba\u03b9\u03bc\u03b1\u03c3\u03c4\u03bf\u03cd\u03bd \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03c0\u03bf\u03b4\u03bf\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03cc\u03c3\u03bf \u03ba\u03b1\u03b9 \u03b7 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03bf\u03c5 \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03b9 \u03c4\u03bf\u03bd predictor. \u0397 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03b4\u03b5\u03bd \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bd\u03b1\u03ba\u03c4\u03ae\u03c3\u03b5\u03b9 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03b1\u03c0\u03cc mixtures \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03c4\u03b7\u03ba\u03b1\u03bd \u03c0\u03bf\u03c4\u03ad. \u038c\u03c4\u03b1\u03bd \u03c4\u03b1 proxy runs \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ac, \u03bf \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03cc\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 AI \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae\u03c2 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03c0\u03c1\u03bf\u03ba\u03b1\u03c4\u03b1\u03c1\u03ba\u03c4\u03b9\u03ba\u03ae \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03ad\u03c1\u03b5\u03b9\u03b1.<\/p>\n<p>\u0397 \u03c9\u03c1\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03c4\u03c1\u03b5\u03b9\u03c2 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2. \u03a0\u03c1\u03ce\u03c4\u03bf\u03bd, \u03b7 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7 \u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03c0\u03bf\u03b9\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c4\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03bc\u03b5 \u03c0\u03bf\u03b9\u03bf\u03c5\u03c2 \u03cc\u03c1\u03bf\u03c5\u03c2. \u0394\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf\u03bd, \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c4\u03b9\u03c2 \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03c3\u03b5 \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1 \u03c0\u03bf\u03c5 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03c4\u03b9\u03c2 \u03bc\u03b5\u03b9\u03ce\u03c3\u03bf\u03c5\u03bd. \u03a4\u03c1\u03af\u03c4\u03bf\u03bd, \u03b4\u03b5\u03bd \u03ba\u03bb\u03b9\u03bc\u03b1\u03ba\u03ce\u03bd\u03b5\u03b9 \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03ad\u03bd\u03b1 predictor \u03b2\u03c1\u03ae\u03ba\u03b5 \u03ad\u03bd\u03b1\u03bd \u03bd\u03b9\u03ba\u03b7\u03c4\u03ae, \u03b1\u03bb\u03bb\u03ac \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b7 \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03b1\u03bd\u03c4\u03ad\u03c7\u03b5\u03b9 \u03c3\u03b5 \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5\u03c2, \u03c4\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03ad\u03c7\u03bf\u03c5\u03bd lineage \u03ba\u03b1\u03b9 \u03c4\u03bf objective \u03b1\u03bd\u03c4\u03b1\u03bd\u03b1\u03ba\u03bb\u03ac \u03c4\u03bf \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd \u03c0\u03bf\u03c5 \u03b8\u03ad\u03bb\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03bd\u03b1 \u03c7\u03c4\u03af\u03c3\u03b5\u03b9.<\/p>\n<p>\u0391\u03c5\u03c4\u03ae \u03b7 \u03c0\u03b5\u03b9\u03b8\u03b1\u03c1\u03c7\u03af\u03b1 \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b5 \u03bc\u03b9\u03b1 <a href=\"https:\/\/twodots.gr\/full-stack-ai-integrated-digital-products\/\">full-stack \u03c0\u03c1\u03bf\u03c3\u03ad\u03b3\u03b3\u03b9\u03c3\u03b7 \u03c3\u03c4\u03b1 AI \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03b1<\/a>: data pipeline, model, evaluation, monitoring \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c9\u03c2 \u03b5\u03bd\u03b9\u03b1\u03af\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1. \u03a4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b9\u03b1 \u03bc\u03b1\u03b3\u03b9\u03ba\u03ae \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b1 datasets, \u03b1\u03bb\u03bb\u03ac \u03bc\u03b9\u03b1 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03c0\u03bf\u03c5 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03be\u03b7\u03b3\u03ae\u03c3\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5 \u03ba\u03ac\u03b8\u03b5 mixture, \u03c4\u03b9 \u03bc\u03ac\u03b8\u03b1\u03bc\u03b5 \u03ba\u03b1\u03b9 \u03c0\u03cc\u03c4\u03b5 \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf \u03b5\u03c5\u03c1\u03ce compute.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-kicker\">AI pilots \u03bc\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/p>\n<p class=\"td-service-cta-title\">\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 \u03c4\u03bf \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03c0\u03c1\u03b9\u03bd \u03ba\u03bb\u03b9\u03bc\u03b1\u03ba\u03ce\u03c3\u03b5\u03c4\u03b5 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf<\/p>\n<p>\u0397 TWO DOTS \u03bf\u03c1\u03b3\u03b1\u03bd\u03ce\u03bd\u03b5\u03b9 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ac AI workflows \u03bc\u03b5 data lineage, \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 proxy tests, business-specific evaluation gates, cost tracking \u03ba\u03b1\u03b9 human review. \u0388\u03c4\u03c3\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf run \u03b1\u03c0\u03b1\u03bd\u03c4\u03ac \u03bc\u03b9\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 \u03b5\u03c1\u03ce\u03c4\u03b7\u03c3\u03b7 \u03b1\u03bd\u03c4\u03af \u03bd\u03b1 \u03ba\u03b1\u03c4\u03b1\u03bd\u03b1\u03bb\u03ce\u03bd\u03b5\u03b9 compute \u03c7\u03c9\u03c1\u03af\u03c2 \u03ba\u03b1\u03b8\u03b1\u03c1\u03cc \u03ba\u03c1\u03b9\u03c4\u03ae\u03c1\u03b9\u03bf.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u0394\u03b5\u03af\u03c4\u03b5 \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><\/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\">Frequently Asked Questions (FAQs)<\/p>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 data mixing \u03c3\u03c4\u03bf LLM pretraining;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03b7 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03b5\u03bd\u03cc\u03c2 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03bf\u03cd training token budget \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 domains \u03cc\u03c0\u03c9\u03c2 web text, code, \u03b5\u03c0\u03b9\u03c3\u03c4\u03b7\u03bc\u03bf\u03bd\u03b9\u03ba\u03ac \u03ac\u03c1\u03b8\u03c1\u03b1, \u03b2\u03b9\u03b2\u03bb\u03af\u03b1 \u03ae \u03bd\u03bf\u03bc\u03b9\u03ba\u03cc \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf data mixing \u03b8\u03b5\u03c9\u03c1\u03b5\u03af\u03c4\u03b1\u03b9 mixture experiment;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03c0\u03b5\u03b9\u03b4\u03ae \u03bf\u03b9 \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b5\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b7 \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03b8\u03c1\u03bf\u03af\u03b6\u03bf\u03c5\u03bd \u03c3\u03b5 \u03ad\u03bd\u03b1. \u0397 \u03b1\u03cd\u03be\u03b7\u03c3\u03b7 \u03b5\u03bd\u03cc\u03c2 domain \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03b1\u03bd\u03b1\u03b3\u03ba\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03bf \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03cc \u03c4\u03c9\u03bd \u03c5\u03c0\u03cc\u03bb\u03bf\u03b9\u03c0\u03c9\u03bd domains.<\/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 Scheff\u00e9 response surface;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03c3\u03bc\u03ad\u03bd\u03bf \u03c3\u03b5 mixture proportions. \u0397 \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03b7 \u03c4\u03ac\u03be\u03b7 \u03b5\u03ba\u03c6\u03c1\u03ac\u03b6\u03b5\u03b9 \u03c1\u03b7\u03c4\u03ac \u03b1\u03bd \u03b6\u03b5\u03cd\u03b3\u03b7 domains \u03b1\u03c0\u03bf\u03ba\u03bb\u03af\u03bd\u03bf\u03c5\u03bd \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd additive \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03c4\u03bf Pile-CC \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf case study;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf Pile-CC \u03c3\u03c5\u03bc\u03bc\u03b5\u03c4\u03b5\u03af\u03c7\u03b5 \u03c3\u03c4\u03b9\u03c2 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 interaction coefficients \u03bc\u03b5 \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03c5\u03bc\u03ad\u03bd\u03b1 domains, \u03ad\u03bd\u03b4\u03b5\u03b9\u03be\u03b7 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03c9\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf validation objective \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03cc\u03c2 \u03ba\u03b1\u03bd\u03cc\u03bd\u03b1\u03c2.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03bf sparse Scheff\u00e9 \u03be\u03b5\u03c0\u03ad\u03c1\u03b1\u03c3\u03b5 \u03c4\u03bf LightGBM;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf LightGBM \u03ae\u03c4\u03b1\u03bd \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03c3\u03c4\u03b1 1M \u03ba\u03b1\u03b9 60M. \u03a3\u03c4\u03bf 1B \u03c4\u03bf Scheff\u00e9 \u03b5\u03af\u03c7\u03b5 \u03bb\u03af\u03b3\u03bf \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b1 point estimates, \u03b1\u03bb\u03bb\u03ac \u03c4\u03b1 confidence intervals \u03b5\u03c0\u03b9\u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03bf\u03bd\u03c4\u03b1\u03bd, \u03ac\u03c1\u03b1 \u03b7 \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03b9\u03c9\u03bc\u03ad\u03bd\u03b7 \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 comparable performance.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 \u03bf I-optimal \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03cc\u03c2;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03c4\u03bf simulation \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2, designs \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 350 runs \u03ad\u03c6\u03c4\u03b1\u03c3\u03b1\u03bd \u03ae \u03be\u03b5\u03c0\u03ad\u03c1\u03b1\u03c3\u03b1\u03bd \u03c4\u03bf ranking reference \u03c4\u03c9\u03bd 512 RegMix runs, \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c0\u03b9\u03bf \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03b9\u03b1\u03ba\u03ac \u03c3\u03b7\u03bc\u03b5\u03af\u03b1 \u03c3\u03c4\u03bf simplex.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0395\u03af\u03bd\u03b1\u03b9 \u03b5\u03b3\u03b3\u03c5\u03b7\u03bc\u03ad\u03bd\u03b7 \u03b7 \u03b5\u03be\u03bf\u03b9\u03ba\u03bf\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 25%;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0395\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 simulation \u03c0\u03bf\u03c5 \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7 fitted RegMix surface, \u03c4\u03b7 noise assumption \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf objective \u03ba\u03b1\u03b9 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03ad\u03b1 \u03b5\u03c0\u03b9\u03b2\u03b5\u03b2\u03b1\u03af\u03c9\u03c3\u03b7 \u03c3\u03b5 \u03ac\u03bb\u03bb\u03bf project.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03cd \u03b1\u03bb\u03bb\u03bf\u03cd \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03af\u03b4\u03b9\u03b1 \u03bb\u03bf\u03b3\u03b9\u03ba\u03ae;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03b5 multimodal, multilingual, domain-adaptive, instruction-tuning \u03ba\u03b1\u03b9 retrieval settings \u03cc\u03c0\u03bf\u03c5 \u03ad\u03bd\u03b1 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc budget \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03c0\u03b7\u03b3\u03ad\u03c2, \u03b3\u03bb\u03ce\u03c3\u03c3\u03b5\u03c2 \u03ae task families.<\/p>\n<\/div>\n<\/details>\n<\/div>\n<\/section>\n<div class=\"td-source-list\">\n<p id=\"piges\" class=\"td-source-list-title\">Sources<\/p>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2608.23922\" target=\"_blank\" rel=\"noopener\">Mao &amp; Du \u2014 Data Mixing as Mixture Experiment: Response Surface Methodology and Optimal Design for Large Language Model Pretraining<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2407.01492\" target=\"_blank\" rel=\"noopener\">Liu et al. \u2014 RegMix: Data Mixture as Regression for Language Model Pre-training<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/sail-sg\/regmix\" target=\"_blank\" rel=\"noopener\">SAIL \u2014 \u0395\u03c0\u03af\u03c3\u03b7\u03bc\u03bf repository \u03c4\u03bf\u03c5 RegMix<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2101.00027\" target=\"_blank\" rel=\"noopener\">Gao et al. \u2014 The Pile: An 800GB Dataset of Diverse Text for Language Modeling<\/a><\/li>\n<li><a href=\"https:\/\/airc.nist.gov\/airmf-resources\/airmf\/5-sec-core\/\" target=\"_blank\" rel=\"noopener\">NIST AI RMF \u2014 Core: Govern, Map, Measure and Manage<\/a><\/li>\n<li><a href=\"https:\/\/www.nist.gov\/publications\/artificial-intelligence-risk-management-framework-generative-artificial-intelligence\" target=\"_blank\" rel=\"noopener\">NIST \u2014 Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf data mixing \u03c3\u03c4\u03b1 LLMs \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 mixture experiments, proxy runs \u03ba\u03b1\u03b9 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf interactions \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03b1\u03ba\u03c1\u03b9\u03b2\u03cc scale-up.<\/p>","protected":false},"author":1,"featured_media":105865,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[199],"tags":[20877,20880,20878,7477,20879],"class_list":["post-98242","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-techniti-noimosyni","tag-data-mixing","tag-experimental-design","tag-llm-pretraining","tag-machine-learning","tag-regmix"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"_links":{"self":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/98242","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/comments?post=98242"}],"version-history":[{"count":1,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/98242\/revisions"}],"predecessor-version":[{"id":105866,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/98242\/revisions\/105866"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media\/105865"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media?parent=98242"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/categories?post=98242"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/tags?post=98242"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}