Core Computational Task in Autoregressive Generation
The fundamental computational task of an autoregressive language model is to model the conditional probability of the next token given the preceding context, Pr(y_i|x, y_{<i}). A key requirement for practical implementation is that this computation must be performed in an efficient manner.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Sequence Evaluation using Log-Probability
An engineer is using a generative language model to decide which of two possible sentences is a more likely completion for the input prompt 'Once upon a time,'. The model can compute various log-probability scores. To select the better completion, which of the following scores should the engineer compare for each candidate sentence?
Debugging a Language Model's Output Score
Rationale for Log-Probability Calculation in Generative Models
Step-by-Step Sequence Log-Probability Computation
Learn After
An autoregressive language model is in the process of generating a response. It has so far produced the token sequence:
['The', 'quick', 'brown']. To determine the very next token, what is the primary probability distribution the model must compute?Evaluating Language Model Generation Strategies
Computational Constraints in Autoregressive Generation
To generate a sequence of text, the fundamental computational step for an autoregressive model is to calculate the joint probability of all potential future tokens at once, conditioned only on the initial prompt.
Learn After
An autoregressive language model is in the process of generating a response. It has so far produced the token sequence:
['The', 'quick', 'brown']. To determine the very next token, what is the primary probability distribution the model must compute?Evaluating Language Model Generation Strategies
Computational Constraints in Autoregressive Generation
To generate a sequence of text, the fundamental computational step for an autoregressive model is to calculate the joint probability of all potential future tokens at once, conditioned only on the initial prompt.
Related
Sequence Evaluation using Log-Probability
An engineer is using a generative language model to decide which of two possible sentences is a more likely completion for the input prompt 'Once upon a time,'. The model can compute various log-probability scores. To select the better completion, which of the following scores should the engineer compare for each candidate sentence?
Debugging a Language Model's Output Score
Rationale for Log-Probability Calculation in Generative Models
Step-by-Step Sequence Log-Probability Computation