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A
A → B → C → D
B
A → C → B → D
C
C → A → B → D
D
B → A → C → D
Understanding the Answer
Let's break down why this is correct
Answer
The process begins by computing attention scores that measure how much each part of the input should influence the rest. These scores are then applied to the input sequence, weighting each element accordingly. The weighted sum produces a single context vector that summarizes the relevant information. Finally, this context vector is fed into the next part of the model for tasks such as prediction or classification. For example, a language model would use the context vector to decide the next word.
Detailed Explanation
The system first calculates attention scores to see how much each part of the input matters. Other options are incorrect because This order puts the context vector right after computing scores, then applies scores after that, which is impossible; It suggests applying scores before they are calculated, which defies logic.
Key Concepts
Attention Mechanisms
Sequence Modeling
Neural Networks
Topic
Attention Mechanisms
Difficulty
hard level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In the context of attention mechanisms, how do they improve model performance in sequence tasks?
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2
Question 2How do attention mechanisms improve sequence modeling in neural networks?
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3
Question 3What is the primary reason attention mechanisms improve the performance of sequence models in capturing relevant information?
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4
Question 4In the context of attention mechanisms, how do they improve model performance in sequence tasks?
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5
Question 5How do attention mechanisms improve sequence modeling in neural networks?
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6
Question 6What is the primary reason attention mechanisms improve the performance of sequence models in capturing relevant information?
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7
Question 7Arrange the following steps in order to describe the functioning of attention mechanisms in sequence modeling: A) Compute attention scores B) Generate context vector C) Apply attention scores to the input sequence D) Use context vector for downstream tasks
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Practice
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