Standard Auto-Regressive Probability Factorization using Embeddings
In standard auto-regressive language models, the joint probability of a token sequence is factored using the chain rule of probability. In neural network implementations, this conditioning on previous tokens is practically achieved by using their embeddings. This relationship for a sequence can be expressed with the following formula, which shows the equivalence between the probabilistic formulation and its neural network counterpart: where represents the embedding of token .
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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
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Role of the [CLS] Token in Sequence Classification
Masked Language Modeling
Input Formatting with Separator Tokens
Standard Auto-Regressive Probability Factorization using Embeddings
CLS Token as a Start Symbol in Encoder Pre-training
Comparison of Context Usage in Causal vs. Masked Language Modeling
Applying the General Sequence Model Formulation
In the general formulation of a sequence model,
o = g(x_0, x_1, ..., x_m; θ), which statement best analyzes the distinct roles of the components?Match each symbol from the general sequence model formulation,
o = g(x_0, x_1, ..., x_m; θ), with its correct description.Fundamental Issues in Sequence Model Formulation
Neural Network as a Parameterized Function
Probability Factorization for Arbitrary Order Token Prediction
Step-by-Step Example of Auto-Regressive Sequence Generation
Standard Auto-Regressive Probability Factorization using Embeddings
A language model is designed to calculate the likelihood of a text sequence by predicting each token based only on the tokens that have come before it. Given the three-token sequence 'The quick brown', which of the following expressions correctly represents how this model would calculate the total probability of the entire sequence?
Example of Auto-Regressive Probability Calculation
Calculating Sequence Probability in an Auto-regressive Model
Debugging a Sequence Probability Calculation
Learn After
Probability Factorization for Arbitrary Order Token Prediction
Causal Language Modeling
An auto-regressive neural network is calculating the joint probability of the token sequence
(x_0, x_1, x_2, x_3). To do this, it must compute the conditional probability for the final token, expressed asPr(x_3 | x_0, x_1, x_2). Which statement best analyzes how the neural network practically implements this probabilistic conditioning?Neural Network Probability Factorization
An auto-regressive neural network is tasked with calculating the total probability of the three-token sequence
(x_0, x_1, x_2). Arrange the following computational steps in the correct chronological order that the model would follow, wheree_irepresents the embedding for tokenx_i.