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Check your knowledge by answering the following questions.

Check your knowledge

1.

What is the purpose of a Large language model (LLM)?

To process and produce natural language text by learning from a massive amount of text data to discover patterns and rules of language.

To exhibit anthropomorphism and understand emotions.

To understand language and facts.

2.

What is the difference between traditional Natural language processing (NLP) and Large language models (LLMs)?

Traditional NLP uses many terabytes of unlabeled data in the foundation model, while LLMs provide a set of labeled data to train the machine-learning model.

Traditional NLP is highly optimized for specific use cases, while LLMs describe in natural language what you want the model to do.

Traditional NLP requires one model per capability, while LLMs use a single model for many natural language use cases.

3.

What is the purpose of tokenization in natural language models?

To represent text in a manner that's meaningful for machines without losing its context, so that algorithms can more easily identify patterns.

To generate text on a letter-by-letter basis.

To represent common words with a single token.

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