Question 1

True or false: Sentiment analysis can perfectly detect sarcasm in online reviews.

Accepted Answer

False. Sarcasm relies on tone and context often lost in text; current models still struggle, with accuracy rarely exceeding 70% in noisy data.

Question 2

True or false: Transformer models require recurrent loops to process sequential text data.

Accepted Answer

False. Transformers use self-attention and positional encodings, not recurrence—they process all tokens in parallel, unlike RNNs.

Question 3

True or false: Early NLP systems used rule-based approaches, not statistical ones, before the 1990s.

Accepted Answer

True. Pre-1990s NLP relied on handwritten rules; statistical methods like n-grams emerged later with more data and computing power.

Question 4

True or false: Word2Vec embeddings guarantee that opposite words like 'hot' and 'cold' have opposite vectors.

Accepted Answer

False. Word2Vec captures similarity, not antonymy; 'hot' and 'cold' may be close in vector space because they appear in similar contexts.

Question 5

True or false: NLP models like BERT can be fine-tuned for specific tasks with just a few hundred examples.

Accepted Answer

True. Pre-trained transformers transfer knowledge effectively; fine-tuning with as few as 500 examples can achieve strong performance on niche tasks.

Question 6

True or false: BERT reads text left-to-right and right-to-left simultaneously during training.

Accepted Answer

True. BERT uses a masked language model and bidirectional attention, processing both directions to grasp context, unlike left-to-right models.

Question 7

True or false: GPT-3 can generate code, but it doesn't actually understand programming logic.

Accepted Answer

True. LLMs predict tokens based on patterns, not true understanding—they mimic reasoning but lack genuine comprehension of logic or semantics.

Question 8

True or false: A single neural network layer can learn human-level grammar without any training data.

Accepted Answer

False. Neural networks require vast data to learn grammar; a single layer lacks depth and cannot capture complex linguistic rules from scratch.

Natural Language Processing Trivia Questions