The Transformer: The New Architecture That Changed Language Processing | Train llm with structured data | Best course on large language models | Large language models examples | Turtles AI

In the paper “Attention Is All You Need” (2017), Vaswani and colleagues at Google propose an architecture based entirely on the self-attention mechanism, eliminating recurrences and convolutions. The Transformer improves parallelization and contextual understanding in sequential tasks.

Key points:

• Introduces an encoder-decoder model without an RNN or CNN.
• Uses scaled self-attention and multi-head attention to weight relationships between tokens.
• Parallelism dramatically reduces training time.
• Superior performance on English-German and -French translations (BLEU 28.4 and 41.8).

In the natural language processing landscape, the paper “Attention Is All You Need” is a milestone. Before it, sequential models – especially RNNs and LSTMs – processed text linearly, requiring a long time and suffering in capturing relationships over long sequences. The Google Brain team led by Vaswani, Shazeer, Parmar, Uszkoreit and others, proposes a different paradigm: a system entirely based on attention mechanisms.

At the core is self-attention, a mechanism by which each token generates Q (query), K (key) and V (value) vectors; the normalized dot product between Q and K assigns weights to the corresponding Vs, allowing the network to modulate the influence of one token on another (ibm.com). Multi-head attention allows you to use multiple “attention strategies” in parallel, sharpening the ability to capture complex relationships in text.

Effectively described by illustrator Jay Alammar, when processing ambiguous words – such as “it” referring to “animal” – the model can directly correlate distant tokens without having to go through a hidden state, a distinctive ability compared to recurrent models.

The architecture is divided into encoder and decoder stacks. Encoders process all input tokens in parallel, enriching them via self-attention and feed-forward. Decoders generate sequences including masking (to prevent a token from looking over) and cross-attention with the encoder. Since there is no sequential dependency, Transformer fully leverages hardware parallelization, making training faster and more efficient.

In direct comparison with RNN and CNN models on machine translation tasks, Transformer achieves BLEU scores of 28.4 (EN-DE) and 41.8 (EN-FR), surpassing the best results at the time with less computational effort. This demonstrates not only effectiveness, but also better scalability: subsequently, scaling to tens or hundreds of billions of parameters has shown continuous improvements.

Since the paper, attention has influenced multiple fields, from pre-trained language models – such as BERT and GPT – to computer vision (Vision Transformer), through applications in audio, robotics, and multimodal systems. The concept of attention has also inspired research on efficient variants (e.g. Linformer, with complexity O(n) vs. O(n²)).

In the Google offices, the choice of the name “Transformer” – recalling the toy series and the hiding of any hierarchical order among the authors – reflects a collaborative and innovative approach, culminating in a paper submitted just before the deadline and becoming a milestone. 

“Attention Is All You Need” introduced a concise and elegant design that prioritizes contextual relationship and parallelized power, outlining a methodology that still inspires the development of deep neural networks today.