>In this case, the most productuce model is to look at as a text generator that produces documents that look like its training data There was no document in GP

>In this case, the most productuce model is to look at as a text generator that produces documents that look like its training data There was no document in GPT-4's training that looked anything like what the transcript of this experiment looked like so that's pretty wrong. It behaves like how people who wrote its training set might have behaved is more accurate not only because of the better model but literally because the predictor's goal is to reverse engineer the casual processes that led to that output. https://www.pnas.org/doi/full/10.1073/pnas.2016239118 https://www.pnas.org/doi/full/10.1073/pnas.2016239118 A Language Model fed on protein sequences has biological structure and function emerge in the inner layers because those are the processes that could have led to that output. If you say, it generated text that looks like its training then why does it not stop at alphanumeric sequences that simply look like protein sequences? Why is it generating actual novel proteins ? https://www.nature.com/articles/s41587-022-01618-2 https://www.nature.com/articles/s41587-022-01618-2 Because looking like the training data is not actually the goal. Modelling it is.