AI Glossary

Transformer

Understanding AI Terminology

The neural network architecture that powers modern large language models.

What It Means

The Transformer is a neural network architecture introduced in 2017 that revolutionized natural language processing. Unlike previous architectures, Transformers use self-attention mechanisms to process all parts of the input simultaneously, enabling them to capture long-range dependencies in text. All major LLMs including GPT, Claude, and Llama are built on Transformer architecture or its variants.

Examples

  • GPT stands for 'Generative Pre-trained Transformer'
  • BERT was an early influential Transformer model
  • Vision Transformers extend the architecture to images

How This Applies to ARKA-AI

All models available through ARKA-AI are based on Transformer architecture, benefiting from its powerful language understanding capabilities.

Frequently Asked Questions

Common questions about Transformer

Transformers can process all words in a sentence simultaneously rather than one at a time, capturing relationships between distant words. This parallelization also enables efficient training on huge datasets.
Attention is a mechanism that lets the model weigh the importance of different words when processing each word. It's what allows models to understand context and meaning across long texts.

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