I believe the term is reasonably appropriate here. The abilities being described here are "emergent" in the sense that the model was not specifically trained f

I believe the term is reasonably appropriate here. The abilities being described here are "emergent" in the sense that the model was not specifically trained for them, but they show up anyway. Your example is about modeling a specific function and having its accuracy increase with model complexity, which is classical ML formulation, but this is not what is happening here. LLMs are trained on a very simple task: given a natural text, predict the next word. But as model complexity and training set sizes increase, they start exhibiting more sophisticated abilities, such as basic forms of reasoning, and contextual memory. In your definition, the parts of the whole are "lots of statistics about text" and the emergent property is "semantic reasoning". Scale is inevitably a part of this: somewhere else in the thread you mention that "liquidity" is an emergent property of H2O, but if you take a handful of H2O molecules they don't behave as a liquid.