Learn this and try the below quiz
1. What type of AI tasks can language models perform?
A) Determining sentiment or classifying natural language text
B) Summarizing text
C) Comparing multiple text sources for semantic similarity
D) Generating new natural language
Explanation: Language models can perform various NLP tasks including determining sentiment, summarizing text, comparing text for semantic similarity, and generating new natural language.
2. What is the architecture that cutting-edge large language models are based on?
A) Transformer
B) Convolutional Neural Network
C) Recurrent Neural Network
D) Decision Tree
Explanation: Today's cutting-edge large language models are based on the transformer architecture.
3. What are the two main components of a transformer model architecture?
A) Encoder and Decoder
B) Input Layer and Output Layer
C) Convolutional Layer and Pooling Layer
D) Hidden Layer and Activation Layer
Explanation: Transformer model architecture consists of an encoder block and a decoder block.
4. What does the encoder block in a transformer model do?
A) Creates semantic representations of the training vocabulary
B) Generates new language sequences
C) Extracts image features
D) Classifies data points
Explanation: The encoder block creates semantic representations of the training vocabulary.
5. What is the purpose of the decoder block in a transformer model?
A) Extracts features from images
B) Generates new language sequences
C) Detects anomalies in data
D) Performs dimensionality reduction
Explanation: The decoder block generates new language sequences.
6. What is tokenization in the context of training a transformer model?
A) Decomposing training text into unique text values (tokens)
B) Generating new sequences of text
C) Extracting features from images
D) Reducing the dimensionality of data
Explanation: Tokenization involves decomposing training text into unique text values (tokens).
7. How are semantic relationships between tokens represented in a transformer model?
A) Embeddings
B) Tokens
C) Labels
D) Clusters
Explanation: Semantic relationships between tokens are represented using embeddings.
8. What is the purpose of attention layers in a transformer model?
A) To quantify the strength of the relationships between tokens
B) To classify images
C) To reduce dimensionality
D) To generate labels
Explanation: Attention layers quantify the strength of the relationships between tokens.
9. How do transformer models handle the prediction of the next token in a sequence?
A) By using attention scores to calculate a possible vector for the next token
B) By clustering similar tokens
C) By reducing the dimensionality of the token vectors
D) By classifying the tokens
Explanation: Transformer models use attention scores to calculate a possible vector for the next token in the sequence.
10. Which technique is used to determine if two vectors have similar directions and therefore represent semantically linked words?
A) Cosine similarity
B) Clustering
C) Dimensionality reduction
D) Classification
Explanation: Cosine similarity is used to determine if two vectors
have similar directions and represent semantically linked words.
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