People fine-tune the superintelligent LLM to do something other than pure prediction, like with ChatGPT. Because it’s “superintelligent”, it has the capabilities buried in there (which is to say, more specifically, it can generate superhumanly-intelligent outputs if conditioned on superhumanly intelligent inputs—I’m not trying to argue this as what will happen, it’s just my interpretation of the assumption of “superintelligent LLM”). So perhaps fine-tuning on a dataset of true answers to hard questions brings this out. Or perhaps using RLHF or something else.
I agree that this isn’t a “one-line bash script”. My interpretation of lc is that “LLM” doesn’t necessarily mean pure prediction (sine existing LLMs aren’t only trained on pure prediction, either); and in particular “superintelligent LLM” suggests that someone found a way to get superhumanly-useful outputs from an LLM (which people surely try to do).
Here’s my answer.
People fine-tune the superintelligent LLM to do something other than pure prediction, like with ChatGPT. Because it’s “superintelligent”, it has the capabilities buried in there (which is to say, more specifically, it can generate superhumanly-intelligent outputs if conditioned on superhumanly intelligent inputs—I’m not trying to argue this as what will happen, it’s just my interpretation of the assumption of “superintelligent LLM”). So perhaps fine-tuning on a dataset of true answers to hard questions brings this out. Or perhaps using RLHF or something else.
I agree that this isn’t a “one-line bash script”. My interpretation of lc is that “LLM” doesn’t necessarily mean pure prediction (sine existing LLMs aren’t only trained on pure prediction, either); and in particular “superintelligent LLM” suggests that someone found a way to get superhumanly-useful outputs from an LLM (which people surely try to do).