This is largely a pointless semantic debate, but to risk wasting my time: transformers specifically in LLMs are doing more than curve fitting as there can never be enough training data to naively interpolate between training examples to construct the space of semantically valid text strings. To find meaningful sentences between training examples, the intrinsic regularity of the underlying distribution must be modeled. This is different from merely curve fitting. To drive this point home, some examinations of transformer behavior in LLMs show emergent structures that capture arbitrary patterns in the input and utilize them in constructing the output. This is not merely curve fitting.