26 Questions
What is the primary function of the Multi-Head Attention mechanism in the Transformer architecture?
To allow the model to focus on different representation subspaces simultaneously
What is the purpose of the Query-Key-Value mechanism in Self-Attention?
To compute the weighted sum of the value vectors
How many layers of the Transformer architecture are repeated?
6
What is the purpose of the Feed Forward Neural Networks in the Transformer architecture?
To transform the output of the self-attention mechanism
What is the function of the Add & Norm component in the Transformer architecture?
To add the output of the self-attention mechanism and the input, and then normalize
What is the purpose of the Positional Encoding in the Transformer architecture?
To preserve the sequential information of the input sequence
What is the function of the Masked Multi-Head Attention mechanism?
To prevent the Decoder from attending to future tokens
What is the purpose of the Embedding layer in the Transformer architecture?
To convert the input sequence into a numerical representation
How does the Decoder component of the Transformer architecture process the input sequence?
One token at a time, sequentially
What is the purpose of the Linear layer in the Transformer architecture?
To transform the output of the Decoder
What is the name of the Transformer-based compiler model that speeds up a Transformer model?
GO-one
What is the relationship between model size, training data, and compute resources in Transformer models?
Power-law relationship
What is the purpose of attention in sequence-to-sequence models?
To allow flexible access to memory
What is the primary component of the Transformer architecture?
Self-Attention Mechanism
What is the function of the encoder in the Transformer architecture?
To encode the input sequence
What is the mechanism used in the Transformer architecture to compute attention weights?
Query-key-value mechanism
What is the advantage of using multi-head attention in the Transformer architecture?
It enables the model to jointly attend to information from different representation subspaces
What is the purpose of the feedforward neural network in the Transformer architecture?
To transform the output of the self-attention mechanism
What is the key benefit of the Transformer architecture in terms of interaction distance?
O(1)
What is the primary function of the Encoder in the Transformer architecture?
To compute self-attention
What is the purpose of the Query, Key, and Value matrices in the Transformer architecture?
To compute the attention weights
What is the function of the Feed Forward Neural Network (FFNN) in the Transformer architecture?
To perform linear transformations
What is the primary difference between masked multi-head attention and regular multi-head attention?
The masking of future tokens
What is the purpose of the Decoder in the Transformer architecture?
To generate output probabilities
What is the role of positional encoding in the Transformer architecture?
To preserve the order of the input sequence
What is the repeat 6x notation in the Transformer architecture?
The number of layers in the Encoder
Study Notes
Transformer Architecture
- The transformer architecture has an encoder and a decoder, each consisting of 6 identical layers.
- Each layer has two sub-layers: multi-head self-attention and a feed-forward neural network.
- The encoder takes in input embeddings and produces output embeddings.
- The decoder takes in output embeddings and produces probabilities.
Transformer Encoder
- The encoder has self-attention as its core building block.
- Self-attention allows each word in the input sequence to interact with every other word.
Impact of Transformers
- Transformers have revolutionized the field of NLP and ML, enabling significant progress in various tasks.
- The transformer architecture has led to the development of powerful models that can match or exceed human-level performance.
History of NLP Models
- Before transformers, recurrent models such as LSTMs were widely used in NLP tasks.
- Recurrent models were used for sequence-to-sequence problems and encoder-decoder models.
- The transformer architecture has replaced recurrent models as the de facto strategy in NLP.
Scaling Laws
- The performance of transformers improves smoothly as model size, training data, and compute resources increase.
- This power-law relationship has been observed over multiple orders of magnitude with no sign of slowing.
Drawbacks and Variants
- There are drawbacks and variants of transformers that will be discussed further.
This quiz covers the basics of transformer-based encoder-decoder models, their impact on NLP and ML, and the differences between recurrence and attention-based models. It also explores the drawbacks and variants of transformers.
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