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Constructing an Input Vector for a Sequence Model
Imagine a model designed to process sentences. For each word, the model needs a single input vector that captures both its meaning and its specific location in the sentence. For the sentence 'The cat sat', explain how the final input vector for the word 'cat' (which is in the second position) is constructed from its two fundamental components.
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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
Science
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A researcher is training a sequence-processing model and observes that while it correctly identifies the meaning of individual words, it consistently fails on tasks where word order is crucial. For example, it treats 'dog bites man' and 'man bites dog' as having the same overall meaning. The researcher suspects an issue in how the initial input vectors are constructed for the model. What is the most probable cause of this issue?
Constructing an Input Vector for a Sequence Model
Calculating a Combined Input Vector
Learnable Absolute Positional Embeddings