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1. I wasn't suggesting that those questions were in the training data. That’s not my contention at all. My contention is that GPT-3 is sampling from patterns it has observed in text to generate more text - and only that. Do you imagine that it has some reasoning process in which it evaluated the effect of a grain of salt on the Andromeda galaxy, before it decided to spit out a formulaic denial of the question’s premise?

2. Thanks for the link to Minerva. Haven’t had a chance to read that, and it’s interesting. It does seem to be a project specifically aimed at getting quantitative reasoning, meaning that there are a lot of architectural priors going into that objective.

3. Your last point is quite reductive and strange. When I engage in a conversation, I don’t just vomit up patterns I’ve seen before. I critically evaluate information I’m taking in, I consider my past experiences, I apply imagination and curiosity, and I decide if I have something to say in response. If I do, I search for the language patterns that seem capable of expressing what I have to say. This process is nothing like a generative model regurgitating plausible but empty blather. I think humans only do that for specific reasons (performatively, to fill page counts, to write placeholder copy, etc.).



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