In the original paper on GPT three, they said large language models are in context learners. And that would be referred to as doing it in zero-shot, that you're not giving it any correct examples of how to translate. But there's another interpretation that I kind of liked better, which is that pre-trained models modeling a like multiverse of fictional documents. When you prompt the model, you're sort of in a superposition of like all possible documents that might continue from this one. It has pre-trained knowledge that it's leveraging there.
This is a special preview episode of The Cognitive Revolution: How AI Changes Everything. Hosted by Erik Torenberg and Nathan Labenz, TCR hosts in-depth interviews with the creators, builders and thinkers pushing the bleeding edge of AI. On this episode, they talk with Riley Goodside, the first Staff Prompt Engineer at Scale AI and expert in prompting LLMs and integrating them into AI applications.
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