Transformers (how LLMs work) explained visually | DL5

About this Video

• Video Title: Transformers (how LLMs work) explained visually | DL5
• Channel: 3Blue1Brown
• Speakers: The speaker's name is not explicitly mentioned in the transcript.
• Duration: 27:14

Introduction

This video provides a visual explanation of how transformers, the core of current AI advancements, function. It focuses on the data flow within a transformer, detailing the process of generating text through repeated prediction and sampling. The video also covers foundational concepts in deep learning to provide context for understanding transformers.

Key Takeaways

• LLMs generate text by repeatedly predicting and sampling the next word: The process involves taking an initial text snippet, predicting the probability distribution of the next word, randomly sampling from that distribution, appending the sample, and repeating.
• Transformers use tokens and embeddings: Input text is broken into tokens (words or parts of words), each represented by a vector (word embedding) encoding its meaning. Words with similar meanings have close vectors.
• Attention blocks allow vectors to interact: These blocks enable the vectors to update their values based on context, allowing the model to understand nuances in word meaning depending on surrounding words.
• Multi-layer perceptrons process vectors in parallel: These blocks perform the same operation on all vectors simultaneously, enabling further processing of the word embeddings.
• Softmax function normalizes output into a probability distribution: This function transforms the output of the transformer into a probability distribution over possible next words, allowing for random sampling. A "temperature" parameter controls the randomness of the sampling.