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Inferring Probability from Model Output
A language model is generating the next word for the phrase 'The dog is very...'. The model considers five potential words: 'friendly', 'big', 'running', 'brown', and 'asleep'. The final word chosen and output by the model is 'friendly'. If the model uses a selection method where only the single candidate with the highest probability score is chosen and all other candidates are discarded, what can you conclude about the probability score of 'friendly' in relation to the other four words?
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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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A language model is generating the next word in a sentence. After calculating the probabilities for several candidate words, it has the following set: 'the' (probability = 0.45), 'a' (probability = 0.25), 'my' (probability = 0.15), and 'your' (probability = 0.10). If the model uses a selection method where only the single most probable candidate is chosen and all others are discarded, which word will be selected for the final output?
Applying the 1-Best Selection Method
Inferring Probability from Model Output