A language model's prediction rule is to select the output with the highest conditional probability. Given the input text 'The ocean is deep and...', the model computes the following probabilities for the next word:
- P('mysterious' | 'The ocean is deep and...') = 0.55
- P('blue' | 'The ocean is deep and...') = 0.30
- P('empty' | 'The ocean is deep and...') = 0.10
- P('loud' | 'The ocean is deep and...') = 0.05
Based on its prediction rule, which word will the model choose?
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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
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
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Next-Word Prediction Model
A language model's prediction rule is to select the output with the highest conditional probability. Given the input text 'The ocean is deep and...', the model computes the following probabilities for the next word:
- P('mysterious' | 'The ocean is deep and...') = 0.55
- P('blue' | 'The ocean is deep and...') = 0.30
- P('empty' | 'The ocean is deep and...') = 0.10
- P('loud' | 'The ocean is deep and...') = 0.05
Based on its prediction rule, which word will the model choose?
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