Inside Transformers: How Language Models Predict Next Words with Attention and Embeddings

Overview

This video provides a visually driven tour of how transformer based language models operate. It traces data flow from input tokens, through embedding vectors and attention interactions, to a final probability distribution over the next word, using GPT-2 and GPT-3 style examples to illustrate the core ideas.

• Tokens are mapped to vectors via an embedding matrix and then enriched by context through attention blocks.
• The model alternates between attention and feed forward blocks to build context and meaning.
• Prediction reduces to a softmax over a vocabulary after a final unembedding step.
• Temperature and sampling control how deterministic or creative the next word choices are.

Overview

This video offers a comprehensive, visually guided walkthrough of how transformer based language models operate. It begins with the basic premise that models like GPT generate the next piece of text by predicting a probability distribution over possible tokens, and then it shows how this prediction is formed as data moves through the network’s layers. The discussion grounds itself in intuitive geometric ideas about word embeddings, attention, and the repeated use of matrix operations that dominate the computation inside these models. Throughout, practical examples drawn from GPT-2 and GPT-3 illustrate how a seed text can produce coherent longer text when the model samples from its own predictions and appends them to the input.

From Tokens to Embeddings

The first step in processing text is to break the input into tokens. Each token is mapped to a high dimensional vector through an embedding matrix. These embeddings are not just representations of individual words but encodings that can absorb surrounding context. The speaker emphasizes that, in GPT3, the vocabulary size is about 50 000 tokens and the embedding dimension is 12 288, resulting in hundreds of millions of parameters in this embedding stage alone. Visualizations show a 3D projection of these high dimensional embeddings, highlighting how words with similar meanings cluster in vector space. This is where the semantic geometry of language begins to emerge, and the embeddings evolve as context flows through the network.

Attention and Contextual Meaning

Following embedding, the vectors pass through an attention block, which allows tokens to talk to one another and update their meanings based on context. The attention mechanism can distinguish how a word like model changes meaning in different phrases, driven by the surrounding words. The video frames attention as a mechanism that directs information flow between tokens, enabling the network to capture dependencies across long distances in the input. The subsequent feed forward block processes all tokens in parallel, acting like a list of questions that are answered for each token, further refining the representations. The talk stresses that almost all computation is performed via matrix multiplications using learned weight matrices, with normalization steps interspersed between blocks.

From Embeddings to the Final Prediction

After stacking attention and feed forward blocks, the network uses the final vector to produce a distribution over next tokens. This is realized by applying a final matrix, the unembedding matrix, to map the last vector in the context to a vocabulary sized vector of logits. A softmax function then converts logits into a probability distribution. If a token is chosen, sampling from that distribution and appending the chosen token to the input creates the next iteration, generating longer passages. The tutorial notes that this process is what underpins the one word at a time generation you see in ChatGPT and other large language models.

Training, Temperatures, and Demos

The video also explains the training context and the role of backpropagation in scaling deep learning models. It discusses how data is formatted as tensors, how the parameter weights are stored in hundreds of matrices, and how the last vector’s projection via the unembedding matrix, followed by softmax, yields the next token probabilities. The presenter provides practical demonstrations of temperature tuning, showing how a higher temperature yields more diverse outputs while a temperature of zero makes the model deterministic and often less interesting. The video references GPT-3 as an example of a large scale autoregressive model and previews the next chapters that will cover attention in more depth, training details, and broader background knowledge.

Key Takeaways

• Transformers rely on a stack of attention and feed forward layers to propagate context through the network.
• Word meanings are encoded in high dimensional embeddings that capture context and relationships between tokens.
• Prediction of the next token is achieved through a unembedding matrix and softmax, with temperature controlling sampling behavior.
• The majority of the computational load in these models is matrix multiplication over tunable weight matrices learned during training.