QUIZ: Understanding LLMs

What is the fundamental task that LLMs are trained to perform? *

Translate between languages

Classify text into categories

Generate images from text

Predict the next token in a sequence

Which component of the transformer architecture allows LLMs to understand context and relationships between words? *

Tokenization

Attention mechanism

Vector embeddings

Softmax function

What is the main difference between pre-training and fine-tuning? *

Pre-training uses more data than fine-tuning

Pre-training is faster than fine-tuning

Pre-training is generic while fine-tuning is task-specific

Pre-training requires human feedback while fine-tuning doesn't

What are tokens in the context of LLMs? *

Security keys for API access

The smallest units of text that LLMs process

The final outputs of the model

The training examples used to teach the model

Which LLM family is best suited for text analysis and classification tasks? *

GPT family

T5 family

LLaMA family

BERT family

What does "temperature" control in LLM outputs? *

The processing speed of the model

The accuracy of the predictions

The length of the generated response

The randomness/creativity of generated text

What is the main advantage of the attention mechanism over traditional sequential processing? *

It's faster to compute

It uses less memory

It can process entire sequences simultaneously and capture long-range dependencies

It requires less training data

Which statement best describes how LLMs "understand" language? *

They recognize statistical patterns and relationships in text data

They have consciousness and truly comprehend meaning like humans

They use pre-programmed rules about grammar and syntax

They memorize all possible text combinations

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