| No. An LLM can only give probabilities of the next token of output. The time to improve an LLM is during design, training, or fine tuning. Once you've got the final weights, the function is "locked in" and doesn't change. However part of the process of learning to predict human output from the internet, literature, etc. causes some deeper learning to occur, potentially even more than in humans, certainly of a different nature. The LLM is communicating through a lossy process, and there is some randomness imposed on its outputs, so results may vary. The nature of the prompt used can trigger some of this deeper learning, and yield better results than you might otherwise get. These weren't put in by design, they are emergent properties of the LLM. For instance "train of thought" prompting has been show to result in better output. Prompt "engineering" is an empirical process of discovering the quirks and hidden strengths in the model. It is entirely possible that there is a super-human set of cognitive skills embedded inside GPT4, Mistral, or even LLAMA. Given sufficient time, there might be some prompting that could expose it and make it usable. Because LLMs aren't "programs" in the traditional sense, you should treat them as if they were an alien intelligence, because that is effectively what they are. They don't understand humans, no matter how well they act like it at times. They are wild beasts, and we haven't figured out how to domesticate them yet. |