Large Language Models · Quiz 1 · Sep 2025

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Question 173 MCQ · 2.0 marks

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To increase the numerical precision of the attention scores.

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To reduce the overall computational cost of the attention mechanism.

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To ensure that the sum of the attention scores for each query equals 1.

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To avoid numerical issues and loss of gradients during training.

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Question 174 MCQ · 2.0 marks

Which of the following is true regarding sinusoidal encoding?

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The encoding vector of words present at even positions in the given text sequence uses sine, and the encoding vector of words present at odd positions uses cosine.

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The encoding vector of words present at odd positions in the given text sequence uses sine, and the encoding vector of words present at even positions uses cosine.

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The encoding vector for each word position contains values computed using both sine (for even dimensions) and cosine (for odd dimensions).

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Question 175 MCQ · 2.0 marks

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‘content’

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‘je’

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‘suis’

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‘heureux’

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Question 176 MCQ · 2.0 marks

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Question 177 MCQ · 2.0 marks

In the context of language model training, teacher forcing is a technique where:

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The correct target tokens from the training data are used as input for the next time step, regardless of the model’s prediction.

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The model’s predictions are used as the input for the next time step, even if they are incorrect.

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The model is forced to learn without any pre-trained weights.

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The model is forced to correct the input data.

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Question 178 MCQ · 2.0 marks

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Question 179 MCQ · 3.0 marks

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Question 180 MCQ · 3.0 marks

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Question 181 MCQ · 3.0 marks

For a vocabulary size of 10, how many beams will there be in greedy search, beam search with beam size 3, and exhaustive search at step 1 and step 3, respectively?

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Step 1: Greedy: 1, Beam: 1, Exhaustive: 1 Step 3: Greedy: 1, Beam: 1, Exhaustive: 1

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Step 1: Greedy: 1, Beam: 3, Exhaustive: 10 Step 3: Greedy: 1, Beam: 3, Exhaustive: 10

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Step 1: Greedy: 1, Beam: 3, Exhaustive: 1 Step 3: Greedy: 1, Beam: 3, Exhaustive: 100

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Step 1: Greedy: 1, Beam: 3, Exhaustive: 10 Step 3: Greedy: 1, Beam: 3, Exhaustive: 1000

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Question 182 MSQ · 2.0 marks

Which of the following decoding strategies is/are inherently non-deterministic?

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Beam

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Greedy

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Nucleus

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Exhaustive

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Question 183 MSQ · 2.0 marks

Which of the following statements about multiple attention heads in a Transformer are true?

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They allow learning diverse local/global relations.

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They reduce memory usage compared to one head.

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Increasing the number of heads while keeping dmodel fixed, decreases the dimensionality handled by each head.

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All heads necessarily learn completely independent information about the input.

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Question 184 MSQ · 4.0 marks

Which of the following statements about the Transformer architecture are true?

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Masking (look-ahead mask) is applied in every decoder self-attention layer, not just the first one.

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Token embeddings are used in the encoder but not in the decoder.

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Positional encoding is required only in the encoder, not in the decoder.

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Token embeddings are used in both the encoder and the decoder.

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Positional encodings are added to the input embeddings in both the encoder and the decoder.

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Question 185 MSQ · 4.0 marks

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Beam search with beam size 2 will keep either A or B as per probability whereas Top-2 sampling will keep both A and B.

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Beam search with beam size 2 will keep both A and B whereas Top-2 sampling will keep either A or B.

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Beam search with beam size 2 will randomly choose any two tokens, whereas Top-2 sampling will always pick the two least probable tokens.

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Beam search with beam size 2 will always keep the top-2 most probable tokens, whereas Top-2 sampling will restrict choices to top-2 tokens and then sample one probabilistically.

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