{"id":127757,"date":"2026-06-30T17:39:17","date_gmt":"2026-06-30T10:39:17","guid":{"rendered":"https:\/\/tino.vn\/blog\/?p=127757"},"modified":"2026-06-30T23:19:17","modified_gmt":"2026-06-30T16:19:17","slug":"transformer-llm-la-gi","status":"publish","type":"post","link":"https:\/\/tino.vn\/blog\/transformer-llm-la-gi\/","title":{"rendered":"Transformer LLM l\u00e0 g\u00ec? Gi\u1ea3i m\u00e3 \u201cb\u1ed9 n\u00e3o\u201d ph\u00eda sau c\u00e1c Chatbot AI"},"content":{"rendered":"\n

N\u1ebfu b\u1ea1n \u0111\u00e3 t\u1eebng th\u1eafc m\u1eafc v\u00ec sao m\u1ed9t chatbot c\u00f3 th\u1ec3 tr\u00f2 chuy\u1ec7n tr\u00f4i ch\u1ea3y, vi\u1ebft v\u0103n b\u1ea3n m\u1ea1ch l\u1ea1c hay l\u1eadp tr\u00ecnh th\u00e0nh th\u1ea1o, c\u00e2u tr\u1ea3 l\u1eddi n\u1eb1m \u1edf ki\u1ebfn tr\u00fac Transformer LLM \u0111\u01b0\u1ee3c Google gi\u1edbi thi\u1ec7u t\u1eeb n\u0103m 2017. V\u1eady c\u1ee5 th\u1ec3 Transformer LLM l\u00e0 g\u00ec? C\u00f9ng Tino kh\u00e1m ph\u00e1 qua b\u00e0i vi\u1ebft d\u01b0\u1edbi \u0111\u00e2y nh\u00e9!<\/strong><\/p>\n\n\n\n

T\u1ed5ng quan v\u1ec1 Transformer LLM<\/strong><\/h2>\n\n\n\n

Transformer LLM l\u00e0 g\u00ec?<\/strong><\/h3>\n\n\n\n

Transformer LLM l\u00e0 t\u00ean g\u1ecdi chung cho c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef l\u1edbn (Large Language Model – LLM) \u0111\u01b0\u1ee3c x\u00e2y d\u1ef1ng d\u1ef1a tr\u00ean ki\u1ebfn tr\u00fac m\u1ea1ng n\u01a1-ron c\u00f3 t\u00ean Transformer. Hi\u1ec3u m\u1ed9t c\u00e1ch \u0111\u01a1n gi\u1ea3n, \u0111\u00e2y l\u00e0 m\u1ed9t h\u1ec7 th\u1ed1ng h\u1ecdc m\u00e1y \u0111\u01b0\u1ee3c hu\u1ea5n luy\u1ec7n tr\u00ean kh\u1ed1i l\u01b0\u1ee3ng v\u0103n b\u1ea3n kh\u1ed5ng l\u1ed3, c\u00f3 kh\u1ea3 n\u0103ng \u0111\u1ecdc hi\u1ec3u ng\u1eef c\u1ea3nh v\u00e0 t\u1ea1o ra v\u0103n b\u1ea3n m\u1edbi gi\u1ed1ng nh\u01b0 con ng\u01b0\u1eddi vi\u1ebft.<\/p>\n\n\n\n

H\u00e3y h\u00ecnh dung Transformer LLM nh\u01b0 m\u1ed9t ng\u01b0\u1eddi \u0111\u1ecdc s\u00e1ch c\u1ef1c k\u1ef3 nhanh, \u0111\u00e3 \u0111\u1ecdc qua h\u00e0ng t\u1ef7 trang t\u00e0i li\u1ec7u, sau \u0111\u00f3 h\u1ecdc \u0111\u01b0\u1ee3c c\u00e1ch d\u1ef1 \u0111o\u00e1n t\u1eeb ti\u1ebfp theo trong m\u1ed9t c\u00e2u d\u1ef1a tr\u00ean to\u00e0n b\u1ed9 ng\u1eef c\u1ea3nh xung quanh, thay v\u00ec ch\u1ec9 nh\u00ecn v\u00e0o t\u1eeb li\u1ec1n tr\u01b0\u1edbc nh\u01b0 c\u00e1c c\u00f4ng ngh\u1ec7 c\u0169.<\/p>\n\n\n

\n
\"Transformer
Transformer LLM l\u00e0 g\u00ec?<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n

\ud83d\udc49 Xem th\u00eam: B\u1ea3ng so s\u00e1nh c\u00e1c AI model theo benchmark<\/a><\/p>\n\n\n\n

L\u1ecbch s\u1eed ra \u0111\u1eddi c\u1ee7a Transformer LLM<\/strong><\/h3>\n\n\n\n

Ki\u1ebfn tr\u00fac Transformer l\u1ea7n \u0111\u1ea7u \u0111\u01b0\u1ee3c c\u00f4ng b\u1ed1 b\u1edfi nh\u00f3m nghi\u00ean c\u1ee9u c\u1ee7a Google trong b\u00e0i b\u00e1o khoa h\u1ecdc mang t\u00ean “Attention Is All You Need” v\u00e0o n\u0103m 2017. C\u00f4ng tr\u00ecnh n\u00e0y \u0111\u01b0\u1ee3c ph\u00e1t tri\u1ec3n ch\u1ee7 y\u1ebfu \u0111\u1ec3 gi\u1ea3i quy\u1ebft c\u00e1c b\u00e0i to\u00e1n chuy\u1ec3n \u0111\u1ed5i chu\u1ed7i \u0111\u1ea7u v\u00e0o th\u00e0nh chu\u1ed7i \u0111\u1ea7u ra, ch\u1eb3ng h\u1ea1n nh\u01b0 d\u1ecbch gi\u1ecdng n\u00f3i hay chuy\u1ec3n v\u0103n b\u1ea3n th\u00e0nh gi\u1ecdng \u0111\u1ecdc. <\/p>\n\n\n\n

Tr\u01b0\u1edbc th\u1eddi \u0111i\u1ec3m n\u00e0y, c\u00e1c m\u1ea1ng n\u01a1-ron h\u1ed3i quy (RNN) – v\u1ed1n l\u00e0 c\u00f4ng ngh\u1ec7 ch\u1ee7 \u0111\u1ea1o \u0111\u1ec3 x\u1eed l\u00fd ng\u00f4n ng\u1eef – t\u1ed3n t\u1ea1i \u0111i\u1ec3m y\u1ebfu l\u1edbn, \u0111\u00f3 l\u00e0: x\u1eed l\u00fd t\u1eebng t\u1eeb m\u1ed9t c\u00e1ch tu\u1ea7n t\u1ef1 n\u00ean t\u1ed1c \u0111\u1ed9 ch\u1eadm v\u00e0 kh\u00f3 n\u1eafm b\u1eaft m\u1ed1i li\u00ean h\u1ec7 gi\u1eefa c\u00e1c t\u1eeb \u1edf xa nhau trong c\u00e2u. Transformer LLM kh\u1eafc ph\u1ee5c ho\u00e0n to\u00e0n nh\u01b0\u1ee3c \u0111i\u1ec3m n\u00e0y nh\u1edd kh\u1ea3 n\u0103ng x\u1eed l\u00fd song song to\u00e0n b\u1ed9 c\u00e2u c\u00f9ng l\u00fac, gi\u00fap vi\u1ec7c n\u1eafm b\u1eaft s\u1eafc th\u00e1i ng\u00f4n ng\u1eef tr\u1edf n\u00ean hi\u1ec7u qu\u1ea3 h\u01a1n nhi\u1ec1u.<\/p>\n\n\n\n

T\u1eeb n\u1ec1n t\u1ea3ng Transformer, h\u00e0ng lo\u1ea1t m\u00f4 h\u00ecnh n\u1ed5i ti\u1ebfng \u0111\u00e3 ra \u0111\u1eddi nh\u01b0 BERT (chuy\u00ean v\u1ec1 hi\u1ec3u ng\u00f4n ng\u1eef), d\u00f2ng GPT (chuy\u00ean v\u1ec1 sinh v\u0103n b\u1ea3n) v\u00e0 sau n\u00e0y ph\u00e1t tri\u1ec3n th\u00e0nh c\u00e1c tr\u1ee3 l\u00fd AI quy m\u00f4 l\u1edbn \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng r\u1ed9ng r\u00e3i ng\u00e0y nay.<\/p>\n\n\n\n

C\u01a1 ch\u1ebf ho\u1ea1t \u0111\u1ed9ng c\u1ee7a Transformer LLM<\/strong><\/h3>\n\n\n\n

C\u01a1 ch\u1ebf t\u1ef1 ch\u00fa \u00fd (Self-Attention)<\/strong><\/h4>\n\n\n\n

Tr\u00e1i tim c\u1ee7a m\u1ecdi Transformer LLM ch\u00ednh l\u00e0 c\u01a1 ch\u1ebf t\u1ef1 ch\u00fa \u00fd, m\u1ed9t kh\u1ea3 n\u0103ng cho ph\u00e9p m\u00f4 h\u00ecnh x\u00e1c \u0111\u1ecbnh t\u1eeb n\u00e0o trong c\u00e2u c\u1ea7n \u0111\u01b0\u1ee3c “ch\u00fa \u00fd” nhi\u1ec1u h\u01a1n khi x\u1eed l\u00fd m\u1ed9t t\u1eeb c\u1ee5 th\u1ec3. V\u00ed d\u1ee5, trong c\u00e2u “Con m\u00e8o n\u1eb1m tr\u00ean th\u1ea3m v\u00ec m\u1ec7t”, h\u1ec7 th\u1ed1ng c\u00f3 th\u1ec3 nh\u1eadn ra “m\u1ec7t” li\u00ean quan tr\u1ef1c ti\u1ebfp \u0111\u1ebfn “con m\u00e8o” ch\u1ee9 kh\u00f4ng ph\u1ea3i “th\u1ea3m”, d\u00f9 hai t\u1eeb n\u00e0y c\u00e1ch xa nhau trong c\u00e2u.<\/p>\n\n\n\n

M\u00e3 h\u00f3a v\u1ecb tr\u00ed (Positional Encoding)<\/strong><\/h4>\n\n\n\n

V\u00ec x\u1eed l\u00fd to\u00e0n b\u1ed9 c\u00e2u c\u00f9ng l\u00fac thay v\u00ec tu\u1ea7n t\u1ef1, Transformer LLM c\u1ea7n th\u00eam th\u00f4ng tin v\u1ec1 v\u1ecb tr\u00ed c\u1ee7a t\u1eebng t\u1eeb \u0111\u1ec3 kh\u00f4ng b\u1ecb nh\u1ea7m l\u1eabn th\u1ee9 t\u1ef1. K\u1ef9 thu\u1eadt m\u00e3 h\u00f3a v\u1ecb tr\u00ed gi\u00fap b\u1ed5 sung d\u1eef li\u1ec7u n\u00e0y, \u0111\u1ea3m b\u1ea3o c\u00e2u “B\u1ed1 th\u01b0\u01a1ng con” v\u00e0 “Con th\u01b0\u01a1ng b\u1ed1” \u0111\u01b0\u1ee3c hi\u1ec3u theo hai ngh\u0129a kh\u00e1c nhau.<\/p>\n\n\n

\n
\"C\u01a1
C\u01a1 ch\u1ebf ho\u1ea1t \u0111\u1ed9ng c\u1ee7a Transformer LLM<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n

Ki\u1ebfn tr\u00fac Encoder \u2013 Decoder<\/strong><\/h4>\n\n\n\n

Theo c\u00e1c nghi\u00ean c\u1ee9u k\u1ef9 thu\u1eadt, Transformer \u0111\u01b0\u1ee3c x\u00e2y d\u1ef1ng d\u01b0\u1edbi d\u1ea1ng m\u1ea1ng n\u01a1-ron truy\u1ec1n th\u1eb3ng s\u00e2u (deep feed-forward neural network), t\u1eadn d\u1ee5ng c\u01a1 ch\u1ebf ch\u00fa \u00fd \u0111\u1ec3 x\u1eed l\u00fd chu\u1ed7i d\u1eef li\u1ec7u, \u0111\u1eb7c bi\u1ec7t hi\u1ec7u qu\u1ea3 trong c\u00e1c nhi\u1ec7m v\u1ee5 x\u1eed l\u00fd ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean. M\u1ed9t s\u1ed1 m\u00f4 h\u00ecnh ch\u1ec9 s\u1eed d\u1ee5ng ph\u1ea7n encoder (nh\u01b0 BERT) \u0111\u1ec3 hi\u1ec3u ng\u00f4n ng\u1eef, trong khi ph\u1ea7n l\u1edbn c\u00e1c Transformer LLM hi\u1ec7n \u0111\u1ea1i nh\u01b0 d\u00f2ng GPT s\u1eed d\u1ee5ng ki\u1ebfn tr\u00fac decoder-only k\u1ebft h\u1ee3p m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef t\u1ef1 h\u1ed3i quy \u0111\u1ec3 sinh v\u0103n b\u1ea3n li\u00ean t\u1ee5c.<\/p>\n\n\n\n

V\u00ec sao Transformer LLM v\u01b0\u1ee3t tr\u1ed9i so v\u1edbi c\u00e1c m\u00f4 h\u00ecnh c\u0169<\/strong><\/h2>\n\n\n\n

L\u1ee3i th\u1ebf c\u1ee7a Transformer LLM<\/strong><\/h3>\n\n\n\n

So v\u1edbi c\u00e1c ki\u1ebfn tr\u00fac tu\u1ea7n t\u1ef1 truy\u1ec1n th\u1ed1ng, Transformer LLM s\u1edf h\u1eefu ba l\u1ee3i th\u1ebf r\u00f5 r\u1ec7t. <\/p>\n\n\n\n