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Cómo los modelos de lenguaje leen el texto, desde el primer paso hasta la siguiente palabra que escriben.
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Fundamentos
Material de estudio general. Cada artículo explica bien una idea, como lo haría un buen capítulo de libro de texto.
Why models read tokens, not letters or words
The trade-off between letters, words and word pieces, and why every modern model settled on pieces, with bytes as a safety net.
How tokenizers are built: BPE, WordPiece and Unigram
Step by step through byte-pair encoding, and how WordPiece and Unigram make different choices.
Tokens beyond text: images, audio and video
How multimodal models cut pictures into patches and sound into frames so they can be read like words.
What are embeddings?
Turning a word into a list of numbers so that similar meanings end up close together.
Latent space: the hidden meaning in numbers
What the dimensions of an embedding capture, why directions carry meaning, and what we can't read directly.
Seeing high dimensions: PCA, t-SNE, UMAP and friends
How to squash hundreds of dimensions into 2 or 3, what each method keeps, and what it quietly throws away.
Attention and the transformer, explained from scratch
How each token looks at the others to update its meaning, and how stacking that idea builds a transformer.
From the last vector to the next word
Logits, softmax, temperature, top-k and top-p: how a model picks what to say next.
Guías del sitio
Recorridos rápidos de lo que muestra cada herramienta de WordCanvas3D, con enlaces a los fundamentos.
What is a token, and why isn’t it a word?
How tokenizers cut text into reusable pieces, and why those pieces rarely line up with words.
Va con: TokenizadorWhy emoji and other languages cost more tokens
UTF-8 bytes, byte-level BPE, and why the same greeting can take 4 tokens or 25.
Va con: TokenizadorHow a word becomes 300 numbers
What word embeddings are, how GloVe, Word2Vec and FastText learn them, and how to measure similarity.
Va con: Explorador de embeddingsPCA vs UMAP: two ways to flatten meaning
Two ways to squeeze 300 dimensions into 3, what each one keeps, and how to read the result.
Va con: Explorador de embeddingsKing − man + woman, explained
Word analogies as arrow arithmetic: what the Playground computes, why it works, and where it breaks.
Va con: Laboratorio de vectores