A developer is working with a language model that generates text by combining its own internal predictions with a probability distribution derived from similar examples in a large external datastore. The developer observes that the model's output is often creative but frequently ignores the specific, factual information present in the retrieved examples. To make the model's output adhere more closely to the facts in the datastore, how should the developer adjust the interpolation coefficient that balances these two distributions?
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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
Empirical Science
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k-NN LM Interpolation Formula
A developer is working with a language model that generates text by combining its own internal predictions with a probability distribution derived from similar examples in a large external datastore. The developer observes that the model's output is often creative but frequently ignores the specific, factual information present in the retrieved examples. To make the model's output adhere more closely to the facts in the datastore, how should the developer adjust the interpolation coefficient that balances these two distributions?
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