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A language model is given the context: 'The chef carefully added the final, crucial ingredient to the simmering stew: a pinch of...'. The model must predict the next word. Below are the conditional probabilities, Pr(next_word | context), calculated by two different models for four possible next words.
| Next Word | Model A Probability | Model B Probability |
|---|---|---|
| salt | 0.65 | 0.20 |
| concrete | 0.02 |
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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
Ch.5 Inference - Foundations of Large Language Models
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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Fundamental LLM Training Objective
LLM Policy as a Probability Distribution
A language model is given the context: 'The chef carefully added the final, crucial ingredient to the simmering stew: a pinch of...'. The model must predict the next word. Below are the conditional probabilities,
Pr(next_word | context), calculated by two different models for four possible next words.Next Word Model A Probability Model B Probability salt 0.65 0.20 concrete 0.02 Mathematical Notation for Text Generation Probability
Evaluating Language Model Suitability
Predicting Next-Word Likelihood
Loss Function for Language Modeling