Contextual Embedding in Language Models

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Questions and Answers

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?

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?

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?

It converts logits to probabilities after the softmax operation Signup and view all the answers

Which of the following best describes the autoregressive next token prediction used in transformers during inference?

Predicting the next token using only previous tokens without masking Signup and view all the answers

What is the primary function of the unembedding layer in a transformer model?

To transform logits into probabilities for word prediction Signup and view all the answers

Which type of embedding helps maintain the order of words in a sequence for a decoder-only transformer?

Position embeddings Signup and view all the answers

What do composite embeddings refer to in the context of transformer models?

A combination of word embeddings and positional embeddings Signup and view all the answers

In a decoder-only transformer, what is the role of the language model head?

To predict the next token based on previous tokens Signup and view all the answers

Which of the following best describes the training purpose of large language models?

To predict the next word based on a large corpus of text Signup and view all the answers

What can be inferred about the operation of decoder-only models, also known as autoregressive models?

They use left-to-right prediction for each token Signup and view all the answers

What is the significance of token embeddings in a transformer model?

They represent the initial mapped input into a continuous space Signup and view all the answers

How do position embeddings contribute to transformer models?

By indicating the sequential order of tokens in an input Signup and view all the answers

Which of the following describes a key feature of sequence-to-sequence models?

They map input sequences directly to output sequences Signup and view all the answers

What is the primary function of token embeddings in Transformers?

They represent individual words or tokens in vector space. Signup and view all the answers

How do composite embeddings enhance representation in Transformers?

By integrating information from word meaning and positions. Signup and view all the answers

What is the role of the unembedding layer in Transformers?

It converts predictions back into the original application tokens. Signup and view all the answers

What do position embeddings contribute to a Transformer model?

They identify the order of tokens in sequences. Signup and view all the answers

What is indicated by the concept of a language model head in Transformers?

It predicts the next token based on previous inputs. Signup and view all the answers

Which of the following best describes static embeddings?

They represent words without considering sentence structure. Signup and view all the answers

Why might a model using transformer architecture have advantages over RNNs?

Transformers can process all input tokens simultaneously. Signup and view all the answers

In the context of language modeling, what are logits?

The unprocessed output probabilities. Signup and view all the answers

How does attention benefit a transformer model?

It enables the model to focus on relevant parts of the input. Signup and view all the answers

Which of the following statements is true about pre-training in large language models?

It helps the model learn general language patterns. Signup and view all the answers

Which aspect of transformer architecture allows it to process longer sequences than RNNs?

Parallel processing of tokens. Signup and view all the answers

What outcome does the attention mechanism directly facilitate in transformers?

Weight assignment among different input tokens. Signup and view all the answers

What does 'Stacked Transformer Blocks' imply in the architecture?

Layering multiple transformer structures for depth. Signup and view all the answers

Which property is a significant limitation of RNNs when compared to Transformers?

Inability to use information from all time steps simultaneously. Signup and view all the answers

Study Notes