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A language model combines a base probability distribution, P_lm, with a retrieval-based distribution, P_knn, to predict the next token. The final probability is calculated by blending these two distributions using an interpolation coefficient, λ = 0.6. Given the distributions below for a small vocabulary, which token will the model select as its final output?
- P_lm: {'wordA': 0.5, 'wordB': 0.4, 'wordC': 0.1}
- P_knn: {'wordA': 0.2, 'wordB': 0.7, 'wordC': 0.1}
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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
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
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A language model combines a base probability distribution, P_lm, with a retrieval-based distribution, P_knn, to predict the next token. The final probability is calculated by blending these two distributions using an interpolation coefficient, λ = 0.6. Given the distributions below for a small vocabulary, which token will the model select as its final output?
- P_lm: {'wordA': 0.5, 'wordB': 0.4, 'wordC': 0.1}
- P_knn: {'wordA': 0.2, 'wordB': 0.7, 'wordC': 0.1}
Analyzing Conflicting Model Predictions
Calculating the Interpolation Coefficient