Rationale for Log-Probability Calculation in Generative Models
A developer is using a large language model to evaluate the quality of a generated sentence (the output) in response to a user's prompt (the input). The developer's colleague suggests that to get a complete score, they must calculate both the log-probability of the input and the log-probability of the output. Explain why this suggestion is incorrect for this specific task and what the model actually needs to compute.
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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
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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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