People outside the NLP research community need to understand that a language model does nothing more than calculate probabilties for the next word given some co

People outside the NLP research community need to understand that a language model does nothing more than calculate probabilties for the next word given some context. The way it learns that probabilistic function can be quite complex, involving billions of parameters, but it's still fundamentally the same. Most of the increase in performance in recent years has come from the ability to train LMs that better generalize this probability function from huge text corpora--functions that better interleave between the datapoints it's trained on. Humans use language with purpose, to complete tasks and communicate with others, but GPT-3 has no more goals or desires than an n-gram model from the 90s. LMs are essentially a faculty for syntactically well-formed or intuitive/system 1 language generation, but they don't seem to be much more.