To increase the numerical precision of the attention scores.
To reduce the overall computational cost of the attention mechanism.
To ensure that the sum of the attention scores for each query equals 1.
To avoid numerical issues and loss of gradients during training.
Which of the following is true regarding sinusoidal encoding?
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.
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.
The encoding vector for each word position contains values computed using both sine (for even dimensions) and cosine (for odd dimensions).
We have a transformer model translating English to French. Suppose the target sequence is:
Target (French): [‘je’, ‘suis’, ‘heureux’]
Suppose the model predicted at time step t:
t = 1: ‘je’
t = 2: ‘content’
t = 3: ‘heureux’
If teacher forcing is used, what will be the decoder input at time step 3?
‘content’
‘je’
‘suis’
‘heureux’
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The IIT Madras BS Large Language Models (LLM) Quiz 1 paper sat on 26 Oct 2025, in the September 2025 term: 20 questions for 50 marks in 120 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.
| Feature | LLM Quiz 1 26 Oct 2025 at a glance |
|---|---|
| Term | September 2025 term |
| Subject | Large Language Models |
| Course code | BSDA5004 |
| Questions | 20 |
| Marks | 50 |
| Duration | 120 min |
| MCQ | 9 |
| MSQ | 5 |
| Numerical | 6 |
| Official paper | IIT M DEGREE AN EXAM QDB2 26 Oct 2025 |
| Negative marking | No negative marking. |
| Updated |
