Deconstructing Next-Token Prediction
An autoregressive language model is in the process of generating a response. So far, it has produced the sequence of tokens: ['The', 'cat', 'sat', 'on']. The model now needs to calculate the probability for the next token. Using the formula , identify what the terms and represent in this specific scenario.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
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Unconventional Formula for Conditional Sequence Probability
An autoregressive language model has generated the phrase 'After the long hike, we were all very'. To determine the next word, the model evaluates several options from its vocabulary. Which of the following calculations best represents the core principle the model uses to decide which word (e.g., 'tired', 'hungry', 'happy') is most likely to come next?
Deconstructing Next-Token Prediction
An autoregressive model is in the process of generating a sentence. So far, it has produced the sequence of words: 'The cat sat on the'. The model is now trying to determine the most probable next word. Which of the following mathematical expressions correctly represents the probability the model is calculating for the specific word 'mat' to be the next word in the sequence?