Natural Language Processing Natural Language Processing MCQ 3

Question 1

Which loss function is most commonly used for training neural machine translation models?

Accepted Answer

Cross-entropy loss

Answer

Cross-entropy loss measures the difference between predicted token probability distributions and the one-hot target distributions during NMT training.

Question 2

What problem does 'label smoothing' address in sequence-to-sequence NLP models?

Accepted Answer

Overconfident predictions that hurt generalization

Answer

Label smoothing replaces hard 0/1 targets with soft distributions, preventing the model from becoming overconfident on training examples.

Question 3

In the ROUGE metric used for text summarization evaluation, what does ROUGE-L specifically measure?

Accepted Answer

Longest common subsequence between candidate and reference

Answer

ROUGE-L measures the longest common subsequence (LCS) between candidate and reference summaries, capturing sentence-level structure.

Question 4

What is 'zero-shot classification' in the context of large language models?

Accepted Answer

Classifying text without any labeled training examples using natural language prompts

Answer

Zero-shot classification uses an LLM's pre-trained knowledge and natural language descriptions to classify text without task-specific training examples.

Question 5

Which component of a transformer architecture is responsible for the 'parallelization advantage' over RNNs?

Accepted Answer

Self-attention mechanism

Answer

Self-attention computes relationships between all token pairs simultaneously, unlike RNNs which process tokens sequentially and cannot be parallelized across time steps.

Natural Language Processing Practice Test

Natural Language Processing Practice Test

Natural Language Processing Natural Language Processing MCQ 3