> Instead, many have deeply underestimated LLMs, saying that after all they were nothing more than somewhat advanced Markov chains, capable, at most, of regurgitating extremely limited variations of what they had seen in the training set. Then this notion of the parrot, in the face of evidence, was almost universally retracted.
I'd like to see this evidence, and by that I don't mean someone just writing a blog post or tweeting "hey I asked an LLM to do this, and wow". Is there a numerical measurement, like training loss or perplexity, that quantifies "outside the training set"? Otherwise, I find it difficult to take statements like the above seriously.
LLMs can do some interesting things with text, no doubt. But these models are trained on terabytes of data. Can you really guarantee "there is no part of my query that is in the training set, not even reworded"? Perhaps we can grep through the training set every time one of these claims are made.
I'd like to see this evidence, and by that I don't mean someone just writing a blog post or tweeting "hey I asked an LLM to do this, and wow". Is there a numerical measurement, like training loss or perplexity, that quantifies "outside the training set"? Otherwise, I find it difficult to take statements like the above seriously.
LLMs can do some interesting things with text, no doubt. But these models are trained on terabytes of data. Can you really guarantee "there is no part of my query that is in the training set, not even reworded"? Perhaps we can grep through the training set every time one of these claims are made.