You are tasked with evaluating a language model's performance on a dataset composed of multiple text sequences. Arrange the following steps in the correct logical order to compute the log-likelihood for each individual sequence in the dataset.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Comprehension in Revised Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
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Maximum Likelihood Training Objective for a Dataset of Sequences
A language model is defined by the following table of conditional log-probabilities, where
<s>is the start-of-sequence token and<eos>is the end-of-sequence token:| Log-Probability | Value | |---|---| |
log Pr(A | <s>)| -0.5 | |log Pr(B | <s>)| -1.5 | |log Pr(B | A)| -0.2 | |log Pr(A | B)| -1.0 | |log Pr(<eos> | A)| -2.0 | |log Pr(<eos> | B)| -0.1 |Given a training dataset
Dcontaining two sequences:- Sequence 1:
(A, B, <eos>) - Sequence 2:
(B, A, <eos>)
Calculate the log-likelihood for each individual sequence in the dataset. Which of the following options correctly lists the results?
- Sequence 1:
Verifying Language Model Performance on a Small Dataset
You are tasked with evaluating a language model's performance on a dataset composed of multiple text sequences. Arrange the following steps in the correct logical order to compute the log-likelihood for each individual sequence in the dataset.