Yeah -- stochastic just implies a probabilistic method. It's just that when you include enough parameters your probabilities start to match the actual space of acceptable results really really well. In other words, we started to throw memory at the problem and the results got better. But it doesn't change the fundamentals of the approach.
In my experience, it's not that the term itself is incorrect but more so people use it as a bludgeoning force to end conversations about the technology. Rather than, what should happen, is to invite nuance about how it can be utilized and it's pitfalls.
Colloquially, it just means there’s no thinking or logic going on. LLMs are just pattern matching an answer.
From what we do know about LLMs we do know that it is not trivial pattern matching, the output formulated is literally by the definition of machine learning itself completely original information not copied from the training data.
