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Predictive Text Model Comparison
A data science team is comparing two simple models for a predictive text feature. The goal is to predict the next word in a sequence. They want to know which model is more effective at predicting the word 'urgent' after the phrase 'request is'.
- Model A bases its prediction on only the single preceding word (e.g., 'is').
- Model B bases its prediction on the two preceding words (e.g., 'request is').
Using the corpus data below, determine which model assigns a higher probability to the word 'urgent' in this context. Show the calculations for both models and state your conclusion.
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Ch.1 Pre-training - 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
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A language model is analyzing a text corpus of 10,000 two-word phrases. The analysis reveals the following counts:
- The word 'deep' is the first word in 400 phrases.
- The word 'learning' is the second word in 250 phrases.
- The specific phrase 'deep learning' occurs 80 times.
Based on this data, what is the probability that the second word of a phrase is 'learning', given that the first word is 'deep'?
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