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Questions and Answers
What is the primary function of the token embeddings in the input layer of a transformer model?
What is the primary function of the token embeddings in the input layer of a transformer model?
- To compress the data for faster processing
- To implement the softmax function
- To add noise to the input data
- To convert the input tokens into numerical vectors (correct)
What is the role of the unembedding layer in a transformer architecture?
What is the role of the unembedding layer in a transformer architecture?
- To generate the final softmax logits from hidden states (correct)
- To predict multiple words at once
- To combine embeddings from various layers
- To perform dimensionality reduction
How does positional embedding enhance the effectiveness of token embeddings in a transformer model?
How does positional embedding enhance the effectiveness of token embeddings in a transformer model?
- By adding semantics to each token
- By incorporating the sequence information of the tokens (correct)
- By compressing the input data into a single vector
- By normalizing the input vector lengths
What function does the language model head perform in a transformer network?
What function does the language model head perform in a transformer network?
Which of the following best describes the autoregressive next token prediction used in transformers during inference?
Which of the following best describes the autoregressive next token prediction used in transformers during inference?
What is the primary function of the unembedding layer in a transformer model?
What is the primary function of the unembedding layer in a transformer model?
Which type of embedding helps maintain the order of words in a sequence for a decoder-only transformer?
Which type of embedding helps maintain the order of words in a sequence for a decoder-only transformer?
What do composite embeddings refer to in the context of transformer models?
What do composite embeddings refer to in the context of transformer models?
In a decoder-only transformer, what is the role of the language model head?
In a decoder-only transformer, what is the role of the language model head?
Which of the following best describes the training purpose of large language models?
Which of the following best describes the training purpose of large language models?
What can be inferred about the operation of decoder-only models, also known as autoregressive models?
What can be inferred about the operation of decoder-only models, also known as autoregressive models?
What is the significance of token embeddings in a transformer model?
What is the significance of token embeddings in a transformer model?
How do position embeddings contribute to transformer models?
How do position embeddings contribute to transformer models?
Which of the following describes a key feature of sequence-to-sequence models?
Which of the following describes a key feature of sequence-to-sequence models?
What is the primary function of token embeddings in Transformers?
What is the primary function of token embeddings in Transformers?
How do composite embeddings enhance representation in Transformers?
How do composite embeddings enhance representation in Transformers?
What is the role of the unembedding layer in Transformers?
What is the role of the unembedding layer in Transformers?
What do position embeddings contribute to a Transformer model?
What do position embeddings contribute to a Transformer model?
What is indicated by the concept of a language model head in Transformers?
What is indicated by the concept of a language model head in Transformers?
Which of the following best describes static embeddings?
Which of the following best describes static embeddings?
Why might a model using transformer architecture have advantages over RNNs?
Why might a model using transformer architecture have advantages over RNNs?
In the context of language modeling, what are logits?
In the context of language modeling, what are logits?
How does attention benefit a transformer model?
How does attention benefit a transformer model?
Which of the following statements is true about pre-training in large language models?
Which of the following statements is true about pre-training in large language models?
Which aspect of transformer architecture allows it to process longer sequences than RNNs?
Which aspect of transformer architecture allows it to process longer sequences than RNNs?
What outcome does the attention mechanism directly facilitate in transformers?
What outcome does the attention mechanism directly facilitate in transformers?
What does 'Stacked Transformer Blocks' imply in the architecture?
What does 'Stacked Transformer Blocks' imply in the architecture?
Which property is a significant limitation of RNNs when compared to Transformers?
Which property is a significant limitation of RNNs when compared to Transformers?
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Study Notes
Contextual Embedding
- Static embeddings represent each word with a fixed vector, regardless of context.
- The sentence "The chicken didn't cross the road because it was too tired" highlights the importance of context.
- The word "it" can have different meanings depending on the context.
- Contextual embeddings capture the dynamic meaning of words based on their surrounding words, resulting in more accurate representations.
- In this example, understanding "it" requires understanding the entire sentence, and its meaning shifts based on the context of the chicken being tired.
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