alt Hacker NewsI can say what I was getting at quite definitively. An LLM is, to an approximation, a pure function. No side effects, no state changes. No neuroplasticity. No working memory. It really is just predicting next tokens based on previous tokens. So the only way to change its output for a given input is to change the input - that is, pack different information into the context window.
Some nondeterminism from the probabilistic token sampling and distributed inference, of course. But sometimes randomly choosing the second most likely next token isn’t going to manifest a cognitive feedback loop.
Yes, but we are really talking about LRMs not LLMs. They can emulate working memory so well that many people even recommend using bunch of .md files (as a sort of out-of-context notes) instead of RAG based on vector DBs. They can maintain context, by compressing it and writing out memories quite autonomously.
So yes, even though not updating their weights, they can learn plenty new things and concepts, and build new models of the world.