The buzz surrounding ChatGPT and large language models (LLMs) is both exciting and fascinating. However, I've noticed a broad misunderstanding about how these models function, leading to assumptions that skew more towards science fiction than the reality of artificial intelligence. Although most of us aren't AI specialists, a basic understanding of these models can help dispel these misconceptions.
Consider this: I give you the word "peanut" and ask you to suggest the next word. Your response—be it "butter" or something less expected—is influenced by a mix of cultural knowledge, common phrases, and personal preferences. As we continue this exercise, your subsequent choices evolve as the context expands.
This mirrors how an LLM like GPT-3 generates text. Each word—or more accurately, "token"—is chosen based on the prior context, just as you chose words following "peanut". The model processes each token as a self-contained request: "Given these previous words, what's the most likely next word?"
The key takeaway is this: LLMs don't learn from individual interactions, nor do they make plans. They're sophisticated statistical machines that produce rich representations of language. At their core, they're executing a complex series of mathematical operations repeatedly. When they're not actively generating tokens, they're not "present" in the way a human mind would be. They're not waiting, thinking, or sleeping any more than a calculator does between operations.
I hope this explanation demystifies the functioning of LLMs. As developers and tech enthusiasts, it's crucial for us to comprehend the workings of these powerful tools as they continue to shape our world.
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