Deep Learning Transformers and Attention 3

Question 1

What is prompt engineering in the context of large language models?

Accepted Answer

Crafting input text to elicit desired model behavior without changing model parameters

Answer

Prompt engineering designs input prompts — including instructions, examples, and context — to guide a frozen LLM toward desired outputs without gradient-based fine-tuning.

Question 2

What is fine-tuning a pretrained Transformer on a downstream task?

Accepted Answer

Continuing training of pretrained weights on labeled task data to adapt representations to the target domain

Answer

Fine-tuning updates the pretrained model's weights on labeled task data, adapting the general-purpose pretraining knowledge to the specific downstream task.

Question 3

What is the attention mask used for in Transformer models during batched training?

Accepted Answer

Preventing attention to padding tokens to avoid polluting representations with pad token information

Answer

Attention masks indicate which positions are real tokens versus padding, preventing the model from incorporating meaningless padding information into its representations.

Question 4

What is a token in the context of Transformer language models?

Accepted Answer

A unit of text (word, subword, or character) that the model processes as a single input element

Answer

Tokens are the discrete units of text that a language model processes, commonly subword units like BPE or WordPiece that balance vocabulary coverage and sequence length.

Question 5

What is RLHF (Reinforcement Learning from Human Feedback) used for in LLM training?

Accepted Answer

Aligning the model's outputs with human preferences by using human ratings as a reward signal

Answer

RLHF fine-tunes a language model using a reward model trained on human preference comparisons, steering the LLM toward helpful, harmless, and honest outputs.

Deep Learning Practice Test

Deep Learning Practice Test

Deep Learning Transformers and Attention 3