Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
It allows the model to assign different weights to different input elements based on their relevance.
B
It reduces the size of the model by simplifying the architecture.
C
It increases the number of training epochs required for fine-tuning.
D
It limits the model's ability to generalize to new tasks.
Understanding the Answer
Let's break down why this is correct
Answer
Self‑attention lets every token look at every other token in a sentence, so the model learns rich, context‑aware representations that are useful for many tasks. During pre‑training, these representations capture general language patterns; when fine‑tuning for a specific task, the model can quickly adapt because it already knows how to combine words in flexible ways. This means the transfer from the large, generic pre‑training data to a small, task‑specific dataset is smoother and requires fewer examples. For instance, a Transformer trained on a huge news corpus can be fine‑tuned on a small sentiment‑analysis set, and thanks to self‑attention it already knows how to weigh sentiment words relative to the rest of the sentence, speeding up learning. Thus, self‑attention makes the representations more transferable and the fine‑tuning phase more efficient.
Detailed Explanation
Self‑attention lets each token in the input see every other token and decide how much it should listen to each one. Other options are incorrect because The belief that self‑attention shrinks the model is mistaken; Some think it makes training take longer.
Key Concepts
Self-Attention
Transfer Learning
Topic
Transformer Architecture
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
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Question 3How did the attention mechanism in the Transformer model revolutionize machine learning applications in the context of communication?
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Question 4Which of the following contributors to the Transformer model is best known for introducing the concept of self-attention, which allows the model to weigh the importance of different words in a sentence?
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Question 5In the context of Transformer architecture, how does self-attention enhance the process of transfer learning?
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Question 6How does the concept of Multi-Head Attention in Transformer Architecture enhance the capabilities of Deep Learning Models in the context of Transfer Learning?
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Question 7How can transfer learning in transformer architecture improve sequence-to-sequence learning, and what ethical considerations should businesses keep in mind when implementing these AI technologies?
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Question 8How did the attention mechanism in the Transformer model revolutionize machine learning applications in the context of communication?
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Question 9Which of the following contributors to the Transformer model is best known for introducing the concept of self-attention, which allows the model to weigh the importance of different words in a sentence?
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