{"id":478509,"date":"2023-08-09T09:33:56","date_gmt":"2023-08-09T09:33:56","guid":{"rendered":""},"modified":"2023-09-05T11:16:56","modified_gmt":"2023-09-05T11:16:56","slug":"pre-trained-language-models","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/pre-trained-language-models\/","title":{"rendered":"M\u00f4 h\u00ecnh ng\u00f4n ng\u1eef \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc"},"content":{"rendered":"<p>C\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc (PLM) l\u00e0 m\u1ed9t ph\u1ea7n quan tr\u1ecdng c\u1ee7a c\u00f4ng ngh\u1ec7 x\u1eed l\u00fd ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean (NLP) hi\u1ec7n \u0111\u1ea1i. Ch\u00fang \u0111\u1ea1i di\u1ec7n cho m\u1ed9t l\u0129nh v\u1ef1c tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o cho ph\u00e9p m\u00e1y t\u00ednh hi\u1ec3u, gi\u1ea3i th\u00edch v\u00e0 t\u1ea1o ra ng\u00f4n ng\u1eef c\u1ee7a con ng\u01b0\u1eddi. PLM \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf \u0111\u1ec3 kh\u00e1i qu\u00e1t h\u00f3a t\u1eeb nhi\u1ec7m v\u1ee5 ng\u00f4n ng\u1eef n\u00e0y sang nhi\u1ec7m v\u1ee5 ng\u00f4n ng\u1eef kh\u00e1c b\u1eb1ng c\u00e1ch t\u1eadn d\u1ee5ng m\u1ed9t kho d\u1eef li\u1ec7u v\u0103n b\u1ea3n l\u1edbn.<\/p>\n<h2>L\u1ecbch s\u1eed ngu\u1ed3n g\u1ed1c c\u1ee7a c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc v\u00e0 s\u1ef1 \u0111\u1ec1 c\u1eadp \u0111\u1ea7u ti\u00ean v\u1ec1 n\u00f3<\/h2>\n<p>Kh\u00e1i ni\u1ec7m s\u1eed d\u1ee5ng c\u00e1c ph\u01b0\u01a1ng ph\u00e1p th\u1ed1ng k\u00ea \u0111\u1ec3 hi\u1ec3u ng\u00f4n ng\u1eef \u0111\u00e3 c\u00f3 t\u1eeb \u0111\u1ea7u nh\u1eefng n\u0103m 1950. B\u01b0\u1edbc \u0111\u1ed9t ph\u00e1 th\u1ef1c s\u1ef1 \u0111\u1ebfn v\u1edbi s\u1ef1 ra \u0111\u1eddi c\u1ee7a t\u00ednh n\u0103ng nh\u00fang t\u1eeb, ch\u1eb3ng h\u1ea1n nh\u01b0 Word2Vec, v\u00e0o \u0111\u1ea7u nh\u1eefng n\u0103m 2010. Sau \u0111\u00f3, c\u00e1c m\u00f4 h\u00ecnh m\u00e1y bi\u1ebfn \u00e1p \u0111\u01b0\u1ee3c gi\u1edbi thi\u1ec7u b\u1edfi Vaswani et al. v\u00e0o n\u0103m 2017, \u0111\u00e3 tr\u1edf th\u00e0nh n\u1ec1n t\u1ea3ng cho PLM. BERT (Bi\u1ec3u di\u1ec5n b\u1ed9 m\u00e3 h\u00f3a hai chi\u1ec1u t\u1eeb m\u00e1y bi\u1ebfn \u00e1p) v\u00e0 GPT (M\u00e1y bi\u1ebfn \u00e1p \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc t\u1ea1o) theo sau l\u00e0 m\u1ed9t s\u1ed1 m\u00f4 h\u00ecnh c\u00f3 \u1ea3nh h\u01b0\u1edfng nh\u1ea5t trong l\u0129nh v\u1ef1c n\u00e0y.<\/p>\n<h2>Th\u00f4ng tin chi ti\u1ebft v\u1ec1 c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc<\/h2>\n<p>C\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc ho\u1ea1t \u0111\u1ed9ng b\u1eb1ng c\u00e1ch \u0111\u00e0o t\u1ea1o m\u1ed9t l\u01b0\u1ee3ng l\u1edbn d\u1eef li\u1ec7u v\u0103n b\u1ea3n. C\u00e1c em ph\u00e1t tri\u1ec3n s\u1ef1 hi\u1ec3u bi\u1ebft to\u00e1n h\u1ecdc v\u1ec1 m\u1ed1i quan h\u1ec7 gi\u1eefa c\u00e1c t\u1eeb, c\u00e2u v\u00e0 th\u1eadm ch\u00ed to\u00e0n b\u1ed9 t\u00e0i li\u1ec7u. \u0110i\u1ec1u n\u00e0y cho ph\u00e9p h\u1ecd t\u1ea1o ra c\u00e1c d\u1ef1 \u0111o\u00e1n ho\u1eb7c ph\u00e2n t\u00edch c\u00f3 th\u1ec3 \u00e1p d\u1ee5ng cho c\u00e1c nhi\u1ec7m v\u1ee5 NLP kh\u00e1c nhau, bao g\u1ed3m:<\/p>\n<ul>\n<li>Ph\u00e2n lo\u1ea1i v\u0103n b\u1ea3n<\/li>\n<li>Ph\u00e2n t\u00edch t\u00ecnh c\u1ea3m<\/li>\n<li>Nh\u1eadn d\u1ea1ng th\u1ef1c th\u1ec3 \u0111\u01b0\u1ee3c \u0111\u1eb7t t\u00ean<\/li>\n<li>D\u1ecbch m\u00e1y<\/li>\n<li>T\u00f3m t\u1eaft v\u0103n b\u1ea3n<\/li>\n<\/ul>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc<\/h2>\n<p>PLM th\u01b0\u1eddng s\u1eed d\u1ee5ng ki\u1ebfn tr\u00fac m\u00e1y bi\u1ebfn \u00e1p, bao g\u1ed3m:<\/p>\n<ol>\n<li><strong>L\u1edbp \u0111\u1ea7u v\u00e0o<\/strong>: M\u00e3 h\u00f3a v\u0103n b\u1ea3n \u0111\u1ea7u v\u00e0o th\u00e0nh vect\u01a1.<\/li>\n<li><strong>Kh\u1ed1i bi\u1ebfn \u00e1p<\/strong>: M\u1ed9t s\u1ed1 l\u1edbp x\u1eed l\u00fd \u0111\u1ea7u v\u00e0o, ch\u1ee9a c\u00e1c c\u01a1 ch\u1ebf ch\u00fa \u00fd v\u00e0 m\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh chuy\u1ec3n ti\u1ebfp ngu\u1ed3n c\u1ea5p d\u1eef li\u1ec7u.<\/li>\n<li><strong>L\u1edbp \u0111\u1ea7u ra<\/strong>: T\u1ea1o ra k\u1ebft qu\u1ea3 cu\u1ed1i c\u00f9ng, ch\u1eb3ng h\u1ea1n nh\u01b0 d\u1ef1 \u0111o\u00e1n ho\u1eb7c v\u0103n b\u1ea3n \u0111\u01b0\u1ee3c t\u1ea1o.<\/li>\n<\/ol>\n<h2>Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc<\/h2>\n<p>Sau \u0111\u00e2y l\u00e0 c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a PLM:<\/p>\n<ul>\n<li><strong>T\u00ednh linh ho\u1ea1t<\/strong>: \u00c1p d\u1ee5ng cho nhi\u1ec1u nhi\u1ec7m v\u1ee5 NLP.<\/li>\n<li><strong>Chuy\u1ec3n ti\u1ebfp h\u1ecdc t\u1eadp<\/strong>: Kh\u1ea3 n\u0103ng kh\u00e1i qu\u00e1t h\u00f3a tr\u00ean nhi\u1ec1u l\u0129nh v\u1ef1c kh\u00e1c nhau.<\/li>\n<li><strong>Kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng<\/strong>: X\u1eed l\u00fd hi\u1ec7u qu\u1ea3 l\u01b0\u1ee3ng l\u1edbn d\u1eef li\u1ec7u.<\/li>\n<li><strong>\u0110\u1ed9 ph\u1ee9c t\u1ea1p<\/strong>: Y\u00eau c\u1ea7u t\u00e0i nguy\u00ean t\u00ednh to\u00e1n \u0111\u00e1ng k\u1ec3 cho vi\u1ec7c \u0111\u00e0o t\u1ea1o.<\/li>\n<\/ul>\n<h2>C\u00e1c lo\u1ea1i m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc<\/h2>\n<table>\n<thead>\n<tr>\n<th>Ng\u01b0\u1eddi m\u1eabu<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<th>N\u0103m gi\u1edbi thi\u1ec7u<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>BERT<\/td>\n<td>Hi\u1ec3u v\u0103n b\u1ea3n hai chi\u1ec1u<\/td>\n<td>2018<\/td>\n<\/tr>\n<tr>\n<td>GPT<\/td>\n<td>T\u1ea1o v\u0103n b\u1ea3n m\u1ea1ch l\u1ea1c<\/td>\n<td>2018<\/td>\n<\/tr>\n<tr>\n<td>T5<\/td>\n<td>Chuy\u1ec3n v\u0103n b\u1ea3n sang v\u0103n b\u1ea3n; \u00e1p d\u1ee5ng cho c\u00e1c nhi\u1ec7m v\u1ee5 NLP kh\u00e1c nhau<\/td>\n<td>2019<\/td>\n<\/tr>\n<tr>\n<td>roberta<\/td>\n<td>Phi\u00ean b\u1ea3n BERT \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u h\u00f3a m\u1ea1nh m\u1ebd<\/td>\n<td>2019<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc, c\u00e1c v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p c\u1ee7a ch\u00fang<\/h2>\n<p><strong>C\u00f4ng d\u1ee5ng<\/strong>:<\/p>\n<ul>\n<li><strong>Thu\u1ed9c v\u1ec1 th\u01b0\u01a1ng m\u1ea1i<\/strong>: H\u1ed7 tr\u1ee3 kh\u00e1ch h\u00e0ng, s\u00e1ng t\u1ea1o n\u1ed9i dung, v.v.<\/li>\n<li><strong>H\u1ecdc thu\u1eadt<\/strong>: Nghi\u00ean c\u1ee9u, ph\u00e2n t\u00edch d\u1eef li\u1ec7u, v.v.<\/li>\n<li><strong>Ri\u00eang t\u01b0<\/strong>: \u0110\u1ec1 xu\u1ea5t n\u1ed9i dung \u0111\u01b0\u1ee3c c\u00e1 nh\u00e2n h\u00f3a.<\/li>\n<\/ul>\n<p><strong>V\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p<\/strong>:<\/p>\n<ul>\n<li><strong>Chi ph\u00ed t\u00ednh to\u00e1n cao<\/strong>: S\u1eed d\u1ee5ng c\u00e1c model nh\u1eb9 h\u01a1n ho\u1eb7c ph\u1ea7n c\u1ee9ng \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u h\u00f3a.<\/li>\n<li><strong>Xu h\u01b0\u1edbng trong d\u1eef li\u1ec7u \u0111\u00e0o t\u1ea1o<\/strong>: Theo d\u00f5i v\u00e0 qu\u1ea3n l\u00fd d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n.<\/li>\n<li><strong>M\u1ed1i quan t\u00e2m v\u1ec1 quy\u1ec1n ri\u00eang t\u01b0 d\u1eef li\u1ec7u<\/strong>: Th\u1ef1c hi\u1ec7n c\u00e1c k\u1ef9 thu\u1eadt b\u1ea3o v\u1ec7 quy\u1ec1n ri\u00eang t\u01b0.<\/li>\n<\/ul>\n<h2>C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 so s\u00e1nh v\u1edbi c\u00e1c thu\u1eadt ng\u1eef t\u01b0\u01a1ng t\u1ef1<\/h2>\n<ul>\n<li><strong>PLM so v\u1edbi c\u00e1c m\u00f4 h\u00ecnh NLP truy\u1ec1n th\u1ed1ng<\/strong>:\n<ul>\n<li>Linh ho\u1ea1t v\u00e0 c\u00f3 kh\u1ea3 n\u0103ng h\u01a1n<\/li>\n<li>Y\u00eau c\u1ea7u nhi\u1ec1u t\u00e0i nguy\u00ean h\u01a1n<\/li>\n<li>T\u1ed1t h\u01a1n trong vi\u1ec7c hi\u1ec3u b\u1ed1i c\u1ea3nh<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2>Quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 c\u1ee7a t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc<\/h2>\n<p>Nh\u1eefng ti\u1ebfn b\u1ed9 trong t\u01b0\u01a1ng lai c\u00f3 th\u1ec3 bao g\u1ed3m:<\/p>\n<ul>\n<li>Thu\u1eadt to\u00e1n \u0111\u00e0o t\u1ea1o hi\u1ec7u qu\u1ea3 h\u01a1n<\/li>\n<li>N\u00e2ng cao hi\u1ec3u bi\u1ebft v\u1ec1 c\u00e1c s\u1eafc th\u00e1i trong ng\u00f4n ng\u1eef<\/li>\n<li>T\u00edch h\u1ee3p v\u1edbi c\u00e1c l\u0129nh v\u1ef1c AI kh\u00e1c nh\u01b0 t\u1ea7m nh\u00ecn v\u00e0 l\u00fd lu\u1eadn<\/li>\n<\/ul>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng ho\u1eb7c li\u00ean k\u1ebft m\u00e1y ch\u1ee7 proxy v\u1edbi c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc<\/h2>\n<p>C\u00e1c m\u00e1y ch\u1ee7 proxy gi\u1ed1ng nh\u01b0 c\u00e1c m\u00e1y ch\u1ee7 do OneProxy cung c\u1ea5p c\u00f3 th\u1ec3 h\u1ed7 tr\u1ee3 PLM b\u1eb1ng c\u00e1ch:<\/p>\n<ul>\n<li>H\u1ed7 tr\u1ee3 thu th\u1eadp d\u1eef li\u1ec7u ph\u1ee5c v\u1ee5 \u0111\u00e0o t\u1ea1o<\/li>\n<li>Cho ph\u00e9p \u0111\u00e0o t\u1ea1o ph\u00e2n t\u00e1n tr\u00ean c\u00e1c \u0111\u1ecba \u0111i\u1ec3m kh\u00e1c nhau<\/li>\n<li>T\u0103ng c\u01b0\u1eddng b\u1ea3o m\u1eadt v\u00e0 quy\u1ec1n ri\u00eang t\u01b0<\/li>\n<\/ul>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1810.04805\" target=\"_new\" rel=\"noopener nofollow\">BERT gi\u1ea3i th\u00edch<\/a><\/li>\n<li><a href=\"https:\/\/openai.com\/blog\/better-language-models\" target=\"_new\" rel=\"noopener nofollow\">GPT-2: M\u00f4 h\u00ecnh ng\u00f4n ng\u1eef t\u1ed1t h\u01a1n<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/vn\/\" target=\"_new\" rel=\"noopener\">D\u1ecbch v\u1ee5 OneProxy<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1706.03762\" target=\"_new\" rel=\"noopener nofollow\">M\u00f4 h\u00ecnh m\u00e1y bi\u1ebfn \u00e1p<\/a><\/li>\n<\/ul>\n<p>Nh\u00ecn chung, c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef \u0111\u01b0\u1ee3c \u0111\u00e0o t\u1ea1o tr\u01b0\u1edbc ti\u1ebfp t\u1ee5c l\u00e0 \u0111\u1ed9ng l\u1ef1c th\u00fac \u0111\u1ea9y s\u1ef1 hi\u1ec3u bi\u1ebft ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean v\u00e0 c\u00f3 c\u00e1c \u1ee9ng d\u1ee5ng v\u01b0\u1ee3t ra ngo\u00e0i ranh gi\u1edbi c\u1ee7a ng\u00f4n ng\u1eef, mang \u0111\u1ebfn nh\u1eefng c\u01a1 h\u1ed9i v\u00e0 th\u00e1ch th\u1ee9c th\u00fa v\u1ecb cho nghi\u00ean c\u1ee9u v\u00e0 ph\u00e1t tri\u1ec3n trong t\u01b0\u01a1ng lai.<\/p>","protected":false},"featured_media":469209,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478509","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Pre-trained Language Models<\/mark>","faq_items":[{"question":"What are Pre-trained Language Models (PLMs)?","answer":"<p>Pre-trained Language Models (PLMs) are AI systems trained on vast amounts of text data to understand and interpret human language. They can be used for various NLP tasks such as text classification, sentiment analysis, and machine translation.<\/p>"},{"question":"What was the historical development of Pre-trained Language Models?","answer":"<p>The concept of PLMs has its roots in the early 1950s, with significant advancements like Word2Vec in the early 2010s and the introduction of transformer models in 2017. Models like BERT and GPT have become landmarks in this field.<\/p>"},{"question":"How do Pre-trained Language Models work?","answer":"<p>PLMs function using a transformer architecture, comprising an input layer to encode text, several transformer blocks with attention mechanisms and feed-forward networks, and an output layer to produce the final result.<\/p>"},{"question":"What are the key features of Pre-trained Language Models?","answer":"<p>The key features include versatility across multiple NLP tasks, the ability to generalize through transfer learning, scalability to handle large data, and complexity, requiring significant computing resources.<\/p>"},{"question":"What types of Pre-trained Language Models exist?","answer":"<p>Some popular types include BERT for bidirectional understanding, GPT for text generation, T5 for various NLP tasks, and RoBERTa, a robustly optimized version of BERT.<\/p>"},{"question":"How can Pre-trained Language Models be used, and what are the problems associated with them?","answer":"<p>PLMs are used in commercial, academic, and personal applications. The main challenges include high computational costs, bias in training data, and data privacy concerns. Solutions include using optimized models and hardware, curating data, and implementing privacy-preserving techniques.<\/p>"},{"question":"What are the main characteristics of Pre-trained Language Models compared to traditional NLP Models?","answer":"<p>PLMs are more versatile, capable, and context-aware than traditional NLP models, but they require more resources for operation.<\/p>"},{"question":"What are the future prospects for Pre-trained Language Models?","answer":"<p>Future prospects include developing more efficient training algorithms, enhancing the understanding of language nuances, and integrating with other AI fields like vision and reasoning.<\/p>"},{"question":"How can proxy servers like OneProxy be associated with Pre-trained Language Models?","answer":"<p>Proxy servers provided by OneProxy can aid PLMs by facilitating data collection for training, enabling distributed training, and enhancing security and privacy measures.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/478509","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/478509\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/469209"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=478509"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}