No, and no goalposts have shifted. What's happened instead is that the claims made by LLM makers keep getting more and more outlandish as time passes, and they do that as a response to criticism that keeps pointing out the shortcomings of their systems. Every new model is presented as a breakthrough [1] and its makers rush to show off the results like "the new model is 100% better than the old one in passing the Bar exam!". You can almost hear the unsaid triumphant question hanging in the air "Are you convinced now? Are we having fun yet?".
We're not. The big deal with LLMs is that they are large enough language models that they can generate fluent, grammatical text that is coherent and keeps to a subject over a very, very long context. We never could do this with smaller language models. Because statistics.
What LLMs can absolutely not do is generate novel text. This is hard to explain perhaps to anyone who hasn't trained a small language model but generativity -the ability to generate text that isn't in a training set- is a property of the tiniest language model, as it is of the largest one [2]. The only difference is that the largest model can generate a lot more text.
And still that is not what we mean by novelty. For example, take art. When ancient humans created art, that was a new thing that had never before existed in the world and was not the result of combining existing things. It was the result of a process of abstraction, and invention: of generalisation. That is a capability that LLMs (as other statistical systems) lack.
The goalposts therefore have not moved because the criticism is as old as nails and the LLM makers have still not been able to comprehensively address it. They just try to ignore it. If the goalposts are here and you're shooting goals over there and then doing a little victory run every time the ball breaks Col. Mustard's windows, that's not the goalposts that have moved, it's you that keeps missing them.
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[1] I'm old enough to remember... GPT-3 and how it blew GPT-2 out of the water; GPT-3.5 and how it blew GPT-3 out of the water; GPT-4 and how it blew GPT-3.5 out of the water... And all the users who would berate you for using the older model since "the new one is something completely different". Every single model. A yuuuge breakthrough. What progress!
[2] Try this. Take the sentence "<start> the cat sat on the mat with the bat as a hat <end>" and generate its set of bi-grams ("<start> the", "the cat", "cat sat", etc.). Then generate permutations of that set. You'll get a whole bunch -14!-1, as in |sentence|! minus the initial one- of sentences that were not in the training set. That's generativity in a tiny language model. That's how it works in the largest also, hard as that may be to believe. It shouldn't. It's a very simple mechanism that is extremely powerful. Large models are simply better at assigning weights to permutations so that the ones more often encountered in a corpus are weighted more.