I don’t disagree with you, but always think the “they’re just predicting the next token” argument is kind of missing the magic for the sideshow.
Yes they do, but in order to do that, LLMs soak up the statistical regularities of just about every sentence ever written across a wide swath of languages, and from that infer underlying concepts common to all languages, which in turn, if you subscribe at least partially to the Sapir-Wharf hypothesis, means LLMs do encode concepts of human cognition.
Predicting the next token is simply a task that requires an LLM to find and learn these structural elements of our language and hence thought, and thus serves as a good error function to train the underlying network. But it’s a red herring when discussing what LLMs actually do.
Yes they do, but in order to do that, LLMs soak up the statistical regularities of just about every sentence ever written across a wide swath of languages, and from that infer underlying concepts common to all languages, which in turn, if you subscribe at least partially to the Sapir-Wharf hypothesis, means LLMs do encode concepts of human cognition.
Predicting the next token is simply a task that requires an LLM to find and learn these structural elements of our language and hence thought, and thus serves as a good error function to train the underlying network. But it’s a red herring when discussing what LLMs actually do.