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A data scientist is working with a sequence model, g(·; θ), for two different projects. In Project A, the goal is to predict the most likely next word in a user's search query. In Project B, the goal is to classify an entire product review as either 'positive' or 'negative'. How would the nature of the model's direct output, o, most likely differ between these two projects?
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Ch.1 Pre-training - 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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Analysis of Sequence Model Outputs for Different Tasks
A data scientist is working with a sequence model,
g(·; θ), for two different projects. In Project A, the goal is to predict the most likely next word in a user's search query. In Project B, the goal is to classify an entire product review as either 'positive' or 'negative'. How would the nature of the model's direct output,o, most likely differ between these two projects?A data scientist is developing two different systems. Match each system's primary task with the most likely structure of its underlying model's direct output.