This podcast delves into the workings of large language models (LLMs), highlighting their core mechanisms. LLMs generate text by predicting the next word in a sequence, assigning probabilities to all potential options. They develop this skill through extensive training on vast datasets, which includes both pre-training on large amounts of text and reinforcement learning guided by human feedback. This process is computationally demanding, relying on specialized hardware like GPUs and transformer architectures that efficiently process text using "attention" mechanisms. The outcome is remarkably fluent and coherent text, although the models' exact behaviors can be difficult to decipher due to the complexity of their training and the sheer number of parameters involved.
