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The problem with LLMs is the answer always sounds right, no matter if it is or isn't. If you already know the answer to a question it is kind of fun to see an L
by jacobsenscott 1y ago
The problem with LLMs is the answer always sounds right, no matter if it is or isn't. If you already know the answer to a question it is kind of fun to see an LLM get lucky and cobble together a correct answer. But they are otherwise useless - you need to do all the same work you would do anyway to check the LLM's "answer".
- dcre 1y agoThere’s a world of difference between “always sounds right, but actually is right 80% of the time” and “always sounds right, but actually is right 99% of the time.” It has seemed clear to me for a while that we are on the way to the latter though a combination of model improvement and boring, straightforward engineering on scaffolding (e.g., spending additional compute verifying answers by trying to produce counterarguments). Model improvement is maybe less straightforward but the trajectory is undeniable and showing no sign of plateauing.