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by peacebeard
222 days ago
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Humans encounter massive numbers of problems in their experience that informs their problem solving. The same is true of LLM. LLMs do not actually have all their training data memorized. I’m not sure what your basis is for saying “LLMs fail if there is a small wrench in the prompt.” They also succeed despite wrenches in the prompt with great regularity. |
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It's not just semantics, the metrics are supposed to tell us the potential of the model. If they can solve extremely hard PhD problems, it should be the case that we're already in the singularity, and they should be solving absolutely everything in whatever field they were trained in, because it's not just PhD level, it's a machine that has a ton of memory, compute and never sleeps. However, once you use these models extensively, it becomes apparent they are just synthesizing data, and not as much understanding it in a way that would allow them to extrapolate into anything else as humans do.
I think this point is a little hard to explain. I'll just emphasize, these are smart systems, and they can do a lot, but there is still a disconnect between, let's say, a PhD level model and a human with a PhD, in the "quality" of what we would call "intelligence" of both entities (human and machine).