- Transformer is a neural network architecture that has fundamentally changed the approach to - Artificial Intelligence. Transformer was first introduced in the seminal paper + The Transformer is a neural network architecture that has fundamentally changed the approach to + Artificial Intelligence. Transformers were first introduced in the seminal paper "Attention is All You Need" - in 2017 and has since become the go-to architecture for deep learning models, powering text-generative + in 2017 and have since become the go-to architecture for deep learning models, powering text-generative models like OpenAI's GPT, Meta's Llama, and Google's - Gemini. Beyond text, Transformer is also applied in + Gemini. Beyond text, Transformers are also applied in game playing, demonstrating its versatility across numerous domains. + >, demonstrating their versatility across numerous domains.
- Fundamentally, text-generative Transformer models operate on the principle of next-token prediction: given a text prompt from the user, what is the most probable next token (a word or part of a word) that will follow this input? The core From 7c7af992d32866cb6c2a282a0acc4053912f2fee Mon Sep 17 00:00:00 2001 From: damoebe <100862528+damoebe@users.noreply.github.com> Date: Sat, 28 Feb 2026 15:58:38 +0100 Subject: [PATCH 2/2] Enhance attention score explanation in Article.svelte Clarify the explanation of the scaling and masking process in attention scores, including the use of padding-masks. --- src/components/article/Article.svelte | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/components/article/Article.svelte b/src/components/article/Article.svelte index ee624a2..55b6cfc 100644 --- a/src/components/article/Article.svelte +++ b/src/components/article/Article.svelte @@ -311,10 +311,11 @@ between all input tokens.