In the standard Transformer Decoder, the Multi-Head Attention layer is "Masked". What is the specific purpose of this mask during training?
To filter out padding tokens to save computation.
To prevent the model from attending to the [CLS] and [SEP] special tokens.
To force the model to focus on the Encoder output rather than the Decoder input.
It has a limited receptive field and cannot capture long-range dependencies directly.
It requires more memory than full attention.
It cannot be parallelized on GPUs.
It causes the gradients to explode.
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The IIT Madras BS Large Language Models (LLM) End Term paper sat on 10 May 2026, in the January 2026 term: 20 questions for 50 marks in 180 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 End Term 10 May 2026 at a glance |
|---|---|
| Term | January 2026 term |
| Subject | Large Language Models |
| Course code | BSDA5004 |
| Questions | 20 |
| Marks | 50 |
| Duration | 180 min |
| MCQ | 10 |
| MSQ | 5 |
| Numerical | 5 |
| Official paper | Large Language Models 10 May 26 |
| Negative marking | No negative marking. |
| Updated |
