> My impression is that the field is more disciplined in terms of knowledge now than it was ~8 years ago - the fundamentals are better understood and more clearly expressed in literature.

Is that really true? That's not my impression at all (though to be fair I haven't been keeping up with current research as much as I used to). My understanding is that there is still hardly any knowledge on what deep learning models (and large language models in particular) actually learn. The loss surfaces are opaque, one still doesn't know why local minima reached by gradient descent tend to generalize fairly well for these models. The latent representations that generative language models learn is, with the exception of the occasional paper that finds some superficial correlations, hardly investigated at all and overall extremely poorly understood.

Very much interested in any references that contradict that sentiment.