Large Language Models explained briefly

Large language models power chatbots by learning to predict the next word in a sequence—turning that prediction into fluent, context-aware responses. At their core, these models don’t pick a single “correct” next word. Instead, they assign probabilities across all possible next words, then generate text by repeatedly sampling from that distribution. That probabilistic sampling helps outputs sound natural, and it also explains why the same prompt can produce different answers even when the underlying computation is deterministic.

Training starts with massive amounts of text, often scraped from the internet. For a model like GPT-3, the transcript notes that reading the training text nonstop would take over 2,600 years; newer, larger models train on far more. The model itself is a mathematical function with hundreds of billions of parameters (also called weights). No human manually sets these weights. They begin randomly, producing gibberish, and are then refined through training.

The basic training loop is next-word prediction. Each training example is fed in with all words except the last; the model predicts what the final word should be. An algorithm called backpropagation adjusts the parameters to make the true next word more likely while pushing down the likelihood of other words. Repeating this process across many trillions of examples improves performance not only on training data but also on previously unseen text.

The computational scale is described as staggering: even if a machine could do one billion additions and multiplications per second, completing the operations needed for training the largest models would take well over 100 million years. That level of compute is only feasible because training uses specialized hardware—GPUs—designed to run many operations in parallel.

A key architectural shift came with the transformer. Before 2017, many language models processed text sequentially, word by word. Transformers instead ingest the whole input at once, enabling parallel processing. They represent each word as a vector of numbers so the training process can operate on continuous values. The transformer’s signature mechanism is attention, which lets these word vectors “talk” to one another and update their meanings based on surrounding context—for instance, adjusting the representation of “bank” depending on whether the context implies a riverbank. A feed-forward neural network adds further capacity to store language patterns.

After multiple iterations of attention and feed-forward processing, the model uses a final function on the last vector to output a probability distribution for the next word. The transcript emphasizes that while the framework is designed, the exact behavior that emerges—what the model predicts in particular situations—is an emergent result of how parameters are tuned during training, making it difficult to pinpoint why specific outputs occur.

Finally, chatbot quality depends on more than pre-training. After auto-completing internet text, models undergo reinforcement learning with human feedback, where workers flag unhelpful or problematic predictions and their corrections reshape the parameters to better match user preferences.