What does LLM stand for in the context of AI?

Large Language Model

Encoder

Decoder

Self-attention mechanism

Feed-forward neural networks

What is the main purpose of the self-attention mechanism in transformers?

To allow the model to weigh the importance of different words in the input sequence when processing each word, capturing contextual relationships.

What is a key advantage of transformer models over Recurrent Neural Networks (RNNs)?

Transformers can process all input tokens in parallel, making them more efficient for training on large datasets and capturing long-range dependencies.

What does BERT stand for?

Bidirectional Encoder Representations from Transformers

What does GPT stand for?

Generative Pre-trained Transformer

What are tokens in the context of LLMs?

Tokens are the basic units of text that an LLM processes. They can be words, subwords, or individual characters, depending on the tokenization method used.

What is the purpose of fine-tuning an LLM?

Fine-tuning adapts a pre-trained model to a specific task or domain by further training it on a smaller, task-specific dataset.

What is prompt engineering in the context of LLMs?

Prompt engineering is the practice of carefully designing and refining input prompts to elicit desired responses from an LLM, optimizing its performance for specific tasks.

What is hallucination in the context of LLMs?

Hallucination refers to the phenomenon where an LLM generates false or nonsensical information that appears plausible but has no basis in fact or the training data.