A text generation method (also called nucleus sampling) that only considers tokens whose cumulative probability exceeds a threshold P.
A text generation method (also called nucleus sampling) that only considers tokens whose cumulative probability exceeds a threshold P. With top-p of 0.9, the model chooses from the smallest set of tokens that together have a 90% probability. More dynamic than top-k sampling and widely used in practice.
A parameter that controls the randomness of a language model's output.
The process of selecting the next token from the model's predicted probability distribution during text generation.
A mathematical function applied to a neuron's output that introduces non-linearity into the network.
An optimization algorithm that combines the best parts of two other methods — AdaGrad and RMSProp.
Artificial General Intelligence.
The research field focused on making sure AI systems do what humans actually want them to do.
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