An ensemble of three language models is used to predict the next token. Each model outputs a probability distribution over a small vocabulary. Based on the principle of averaging these distributions to determine the final output, which token should be selected?
Model 1: {'mat': 0.5, 'rug': 0.2, 'floor': 0.2, 'chair': 0.1} Model 2: {'mat': 0.1, 'rug': 0.6, 'floor': 0.2, 'chair': 0.1} Model 3: {'mat': 0.3, 'rug': 0.3, 'floor': 0.3, 'chair': 0.1}
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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
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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Predicting the Next Token with an Ensemble
An ensemble of three language models is used to predict the next token. Each model outputs a probability distribution over a small vocabulary. Based on the principle of averaging these distributions to determine the final output, which token should be selected?
Model 1: {'mat': 0.5, 'rug': 0.2, 'floor': 0.2, 'chair': 0.1} Model 2: {'mat': 0.1, 'rug': 0.6, 'floor': 0.2, 'chair': 0.1} Model 3: {'mat': 0.3, 'rug': 0.3, 'floor': 0.3, 'chair': 0.1}
Evaluating the Averaging Method for Model Ensembling