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Averaging Probability Distributions in LLM Ensembling
A straightforward method for ensembling in Large Language Models involves calculating the average of the next-token probability distributions from each individual model. The token with the highest resulting averaged probability is then selected for the final output sequence.
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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
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A team is developing a system to generate summaries of scientific articles. They are using two different language models. Model A is highly accurate with technical terminology but often produces summaries that are grammatically awkward. Model B excels at generating fluent, well-structured prose but sometimes misinterprets complex technical terms. The team is considering a strategy where they generate a summary from each model for every article and then combine the outputs to create a final version. Which of the following statements provides the most accurate rationale for why this combined approach is likely to be effective?
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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