Large language models like GPT 3.5 operate on an autoregressive basis, predicting the next word based on an extensive context of up to 3000 words. Despite the daunting number of potential combinations, these models achieve remarkable efficiency through significant compression, allowing them to generalize effectively from vast amounts of data. This compression leads to intriguing properties in the completions generated, highlighting the sophistication of modern AI language processing.
