Comment by hnfong

> I assumed that was more the result of doing math on the properties the model generated for the tokens king, man, queen, etc.

This isn't wrong, the model does do a bunch of "math" (HUGE matrix and vector multiplications) on the vectors generated by the tokens, but it's not like the model has any recognizable process that resembles our own reasoning process (unless you ask it to explain "its own reasoning", but the fact that the model explains it in terms of human reasoning doesn't mean that's the way it works internally -- as of today LLMs do not have introspection capabilities).

I suspect most people who haven't thought very deeply about what computation, reasoning and intelligence is would need some time to come to terms to what generative AI is telling us about them.

If it helps, do note that the models have been trained on terrabytes of training data, and the model has "learnt" a bunch of patterns that it could apply to "king+man-woman=" to come to "queen" as the answer. We could even speculate that the word2vec vectors for king and queen would have some dimensions that roughly translates to "class" (monarch), and "gender" (same for man and woman), and it would be relatively straightforward for the model to grab the token that has a signal on those same dimensions. But that's kind of a crude way to look at LLMs since the billions of parameters aren't there only to make money for nVidia, so the actual processes involve much, much, much more compute that human minds would not be able to comprehend.