General Notation for Conditional Probability in Sequence Generation
The notation Pr(y|x, dots, x, y, dots, y) represents the conditional probability of a subsequent item occurring, given a context of preceding items, which can include both initial inputs ('s) and previously generated outputs ('s). This is a general way to express the core calculation in sequence generation models, where the probability of the next item depends on everything that came before it.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.2 Generative Models - Foundations of Large Language Models
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Classification of Long Sequence Modeling Problems
A user provides the input 'Translate this to Spanish: The sky is blue' to a language model. The model, which has a specific set of learned weights and biases, generates the output 'El cielo es azul.' In the context of the notation for text generation probability, Pr_θ(y|x), which of the following correctly identifies the components of this interaction?
Evaluating Model Outputs with Probabilistic Notation
A language model is tasked with summarizing a news article. Match each component of the probabilistic notation used to describe this process with its corresponding role in the summarization task.
General Notation for Conditional Probability in Sequence Generation
Learn After
A language model is generating text and has so far produced the sequence 'The sky is'. The model now needs to calculate the likelihood of the next word being 'blue'. Which of the following mathematical expressions correctly represents the probability of the next word being 'blue', given the preceding words?
Conditional Probability in Sequence-to-Sequence Generation
Notation for Machine Translation Probability
Formula for Re-weighting a Probability Distribution with a Reward Function
Applying Conditional Probability Notation in Text Summarization