Large language models seem startlingly intelligent. But what’s really happening under the hood?
. “We don’t know much about it, except that it is extremely powerful and offers us bedazzling gifts but could also hack the foundations of our civilization.”
Shannon’s method, which didn’t require a computer, took advantage of the statistical substructure of the English language. He started by choosing the word “the” as the seed for a new sentence. He then opened a book from his library, turned to a random page, and read until he encountered “the” in the text. At this point, he wrote down the word that came next—it happened to be “head.
This voting approach allows us to make use of near-matches. For example, we might want the phrase “Mary had a little lamb” to give our program some sort of preference for “lamb,” because “had a little” is similar to our target phrase, “had a small.” We can accomplish this usingfor calculating the similarity of different phrases, and then using these scores to assign votes of varying strength.
We now face a new problem in our thought experiment: the total number of rules we need to address all possible user requests is immense. No collection of humans, no matter how dedicated, could ever come up with the full range required; our system, if it were to work as well as ChatGPT, would need a Borgesian library filled with rules tailored for a near-infinite number of esoteric topics, themes, styles, and demands.
The numbers involved here are huge. Though OpenAI hasn’t released many low-level technical details about ChatGPT, we do know that GPT-3, the language model on which ChatGPT is based, was trained on passages extracted from an immense corpus of sample text that includes much of the public Web. This allowed the model to define and nudge a lot of rules, covering everything from “Seinfeld” scripts to Biblical verses.
There are, of course, mind-numbing technical terms and complex concepts lurking behind all of these basic components. The layers are actually called transformer blocks, and they combine standard feed-forward neural networks with a cutting-edge technique known as multi-headed self-attention.
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