Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
It uses attention mechanisms exclusively
B
It relies heavily on recurrent layers
C
It processes data in a strictly sequential manner
D
It requires convolutional layers for feature extraction
Understanding the Answer
Let's break down why this is correct
Answer
The Transformer uses self‑attention to look at all words in a sentence at once, rather than processing them one after another as in RNNs or LSTMs. Because every word can directly “talk” to every other word, the model learns long‑range relationships quickly and can be trained in parallel on a GPU. It adds a positional encoding to give each word a sense of order, so the model still respects the sequence without sequential steps. This design lets Transformers handle long sentences faster and with more accurate context than older sequential models. For example, in the sentence “The cat sat on the mat,” the Transformer can instantly relate “cat” and “mat” even though they are far apart, something a simple RNN would struggle to do efficiently.
Detailed Explanation
Transformers use attention, a method that looks at all parts of the input at once. Other options are incorrect because Some think Transformers need many recurrent layers to remember past words; It might seem Transformers must read the input one token at a time.
Key Concepts
Attention Mechanisms
Parallel Processing
Machine Translation
Topic
Transformer Architecture
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1What is the primary reason that the Transformer architecture has revolutionized natural language processing compared to earlier models?
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Question 2How does the Transformer architecture enhance parallelization compared to traditional RNNs?
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Question 3Order the steps of how the Transformer architecture processes input data from initial encoding to final output generation.
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Question 4Which of the following statements correctly describe the advantages of the Transformer architecture? Select all that apply.
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Question 5What is the primary reason that the Transformer architecture has revolutionized natural language processing compared to earlier models?
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Question 6How does the Transformer architecture enhance parallelization compared to traditional RNNs?
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Question 7Order the steps of how the Transformer architecture processes input data from initial encoding to final output generation.
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Question 8What distinguishes the Transformer architecture from previous models in handling sequential data?
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Question 9Which of the following statements correctly describe the advantages of the Transformer architecture? Select all that apply.
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