What bearing does my publication history have on the hot-take by someone commenting outside of their (sub)field that clearly don't understand the basic operatio

What bearing does my publication history have on the hot-take by someone commenting outside of their (sub)field that clearly don't understand the basic operation of the mechanism they're commenting on? The author of that text is simply mistaken about the basic operation of the system, thinking that the sampling is added to imitate human behavior. It isn't. You can see the same structure in things as diverse as wavenet-- a feedforward cnn rather than a transformer-- and for the same reason, if you feed back only the top result you rapidly fall into a local minima of the network that gives garbage output. Another more statistical way of looking at it is that the training process produces (or, rather, approaches) the target distribution of outputs even without any lookahead, but it can't do that if it selects the most likely symbol every time because in the real distribution (if we could evaluate it) there are necessarily some outputs which are likely but have prefixes which are unlikely relative to other prefixes of the same length. If you never sample unlikely prefixes you can't reach likely longer statements that start with them. To give a silly example: "Colorless green ideas sleep furiously" is a likely English string relative to its length which GPT3 should have no problem producing (and, in fact, it produces it fine for me). But the prefix "Colorless green" without the rest is just nonsense-- extremely unlikely compared to many other strings of that length. [Not the best example, however, because the prevalence of that specific nonsense statement is so great that GPT3 is actually prone to complete it as the most likely continuation even after just the word colorless at the beginning of a quote. :P but I think it still captures the idea.] If you derandomized GPT* by using a fixed random seed for a CSPRNG to make the sampling decisions every time, the results would be just as good as the current results and it would give a consistent answer every time. For applications other than data compression doing so would be no gain, and would take away the useful feature of being able to re-try for a different answer when you do have some external way of rejecting inferior results. In theory GPT without sampling could still give good results if it used a search to look ahead, but it appears that even extraordinary amounts of computation for look-ahead still is a long way from reaching the correct distribution, presumably because the exponential fan out is so fast that even 'huge' amounts of lookahead are still only testing a tiny fraction of the space.