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submitted 1 year ago by [email protected] to c/[email protected]

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Over just a few months, ChatGPT went from correctly answering a simple math problem 98% of the time to just 2%, study finds. Researchers found wild fluctuations—called drift—in the technology’s abi...::ChatGPT went from answering a simple math correctly 98% of the time to just 2%, over the course of a few months.

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[–] [email protected] 4 points 1 year ago

Getting information into and out of those domains benefits from better language models. Suppose you have an excellent model for solving math problems. It's not very useful if it rarely correctly understands the problem you're trying to solve, or cannot explain the solution to you in a meaningful way.

A similar way in which language models are already used today, is to use their predictive capabilities to infer from your question which model(s) might be useful in responding, gather additional relevant information, and to repackage this information as suitable inputs to more specialized models or external systems.