Natural Language Processing Natural Language Processing MCQ 4

Question 1

What is 'perplexity' as used to evaluate language models?

Accepted Answer

The exponentiated average negative log-likelihood per token

Answer

Perplexity equals 2^(cross-entropy) and measures how well a probability model predicts a sample — lower perplexity indicates a better model.

Question 2

In the context of question answering, what is a 'reading comprehension' task?

Accepted Answer

Answering questions by extracting or inferring information from a provided passage

Answer

Reading comprehension tasks require models to answer questions using information contained in a provided context passage, as in SQuAD.

Question 3

What does the 'temperature' parameter control during language model text generation?

Accepted Answer

The sharpness or randomness of the output token probability distribution

Answer

Temperature scales logits before softmax; lower values make distributions peakier (more deterministic) while higher values increase randomness.

Question 4

Which NLP technique is specifically designed to handle long-range dependencies more effectively than standard RNNs?

Accepted Answer

Long Short-Term Memory (LSTM)

Answer

LSTMs use gating mechanisms (input, forget, output gates) to selectively retain or discard information across long sequences, addressing the vanishing gradient problem.

Question 5

What is 'data augmentation' in the context of NLP model training?

Accepted Answer

Artificially increasing training data diversity through techniques like back-translation or synonym replacement

Answer

NLP data augmentation creates new training examples by modifying existing ones via techniques like paraphrasing, back-translation, or token insertion/deletion.

Natural Language Processing Practice Test

Natural Language Processing Practice Test

Natural Language Processing Natural Language Processing MCQ 4