Have you ever wondered how large language models (LLMs) can predict the next word in a sentence with such accuracy? It’s fascinating to think about how they can understand the context and generate text that makes sense.
A recent visualization has shed some light on this process, showing how LLMs use probability distributions to predict the next word. It’s a complex process, but essentially, the model generates a probability distribution over all possible next words, and then selects the one with the highest probability.
This process is repeated for each word in the sentence, allowing the model to generate coherent and context-dependent text. It’s an impressive feat of artificial intelligence, and has many potential applications in areas such as language translation, text summarization, and chatbots.
If you’re interested in learning more, I recommend checking out the visualization and the accompanying video that explains the process in more detail.