Overview of Large Language Models

Overview

This lecture explains how large language models (LLMs), especially transformers, predict the next word in a text, how they are trained, and why their scale and behavior are unique.

How Language Models Predict Text

• Large language models predict the next word in a text by assigning probabilities to all possible next words.
• Chatbots use LLMs to generate responses by predicting one word at a time, creating natural conversation.
• While deterministic, the model can select less likely words at random, making each response potentially unique.

Training Language Models

• Models are trained with vast amounts of text, often scraped from the internet.
• Training adjusts internal continuous values called parameters or weights, often numbering in the hundreds of billions.
• Training starts with random parameters and refines them using actual text examples.
• The algorithm compares predicted words with actual text and tweaks parameters using backpropagation to increase accuracy.
• The sheer amount of computation required is enormous and only feasible with specialized hardware (GPUs).

Pre-training and Fine-tuning

• The initial stage, called pre-training, teaches the model to autocomplete random text passages.
• To become effective assistants, models undergo reinforcement learning with human feedback (RLHF), where humans flag and correct model errors.

Transformers and Attention

• Transformers revolutionized LLMs by processing text in parallel rather than word by word.
• Words are encoded as lists of numbers (vectors) since training only works with numerical values.
• Transformers use the attention mechanism, letting words influence each other's meaning based on context.
• Another key operation is the feed-forward neural network, increasing the model's capacity to learn patterns.
• Each processing layer refines word representations to improve next word predictions.

Model Behavior and Limitations

• Model behavior emerges from the tuning of billions of parameters, making their decisions hard to interpret.
• Output is often fluent and useful, but the exact reasons for predictions are difficult to pinpoint.

Key Terms & Definitions

• Large Language Model (LLM) — A model that predicts the next word in text, trained on massive datasets.
• Parameter/Weight — A continuous value inside a model, adjusted during training to improve predictions.
• Backpropagation — The algorithm used to update parameters in neural networks based on prediction errors.
• Transformer — A neural network architecture that uses attention and processes text in parallel.
• Attention — An operation allowing words to influence each other's meaning in context.
• Feed-forward Neural Network — A type of neural network layer for learning patterns in data.
• Reinforcement Learning with Human Feedback (RLHF) — Training where humans correct model outputs to refine predictions.
• GPU — A chip specialized for performing many operations in parallel, essential for training large models.

Action Items / Next Steps

• Watch the suggested deep learning series for detailed explanations of transformers and attention.
• View the lecturer’s talk for further insights into LLMs.