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
Core Computational Task in Autoregressive Generation
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.