Schematic of Probability Calculation in Causal Language Modeling
This schematic illustrates the sequential probability calculation in Causal Language Modeling, a type of auto-regressive model. For a sequence , the model predicts each token based on the embeddings of the tokens that came before it. The process begins by setting the probability of the first token, , to 1. Each subsequent token's probability is then conditioned on the embeddings of all prior tokens, as shown in the diagram below. This unidirectional, step-by-step dependency is a core feature of causal language models.
Token: x0 x1 x2 x3 x4 ↓ ↓ ↓ ↓ ↓ Probability: Pr(x0)=1 Pr(x1|e0) Pr(x2|e0, e1) Pr(x3|e0, e1, e2) Pr(x4|e0, e1, e2, e3)
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Computing Sciences
Foundations of Large Language Models Course
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Schematic of Probability Calculation in Causal Language Modeling
An autoregressive language model is given the sequence of tokens: 'The', 'cat', 'sat', 'on', 'the'. It is now tasked with predicting the very next token. Which of the following expressions correctly represents the primary calculation the model performs to determine the likelihood of the word 'mat' appearing next?
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Schematic of Probability Calculation in Causal Language Modeling
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Example of Causal Language Modeling
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