So they quote an expert who says there's a distinction between bona fide novel structures and states, versus statistical heuristics that benefit from the scaling. Meanwhile, yet people claim ChatGPT has learned this or that capability, and it is true "by inspection".
Recent findings like these suggest at least two possibilities for why emergence occurs, said Ellie Pavlick, a computer scientist at Brown University who studies computational models of language. One is that, as suggested by comparisons to biological systems, larger models truly do gain new abilities spontaneously. “It may very well be that the model has learned something fundamentally new and different that it didn’t have at a smaller size,” she said. “That’s what we’re all hoping is the case, that there’s some fundamental shift that happens when models are scaled up.”
The other, less sensational possibility, she said, is that what appears to be emergent may instead be the culmination of an internal, statistics-driven process that works through chain-of-thought-type reasoning. Large LLMs may simply be learning heuristics that are out of reach for those with fewer parameters or lower-quality data.
But, she said, finding out which of those explanations is more likely hinges on a better understanding of how LLMs work at all. “Since we don’t know how they work under the hood, we can’t say which of those things is happening.”