Question 1
What happens when you use Batch Normalization with a batch size of 1?
It works perfectly fine
The statistics become meaningless (mean=0, std=0)
It automatically switches to Layer Normalization
It uses the running statistics from training
Question 2
A language model outputs the following logits for the next token: {cat: 3.2, dog: 2.9, bird: 1.1, fish: 0.5, snake: -0.4} Before sampling, the decoding pipeline performs: • Temperature scaling with T = 2.0 • Top-K filtering with K = 3 After applying both steps, which tokens remain eligible for sampling?
Only ''cat''
'cat'' or ''dog''
'cat'', ''dog'' or ''bird''
'dog'', ''bird'' or ''fish''
Question 3
Tokens A, B, C, D, E
Tokens A, B
Tokens A, B, C
Tokens A, B, C, D
Tokens A, B, D
21 more questions in this paper
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More on the LLM End Term 21 Dec 2025 Set 1 paper
The IIT Madras BS Large Language Models (LLM) End Term paper sat on 21 Dec 2025, in the September 2025 term, set 1: 24 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 21 Dec 2025 Set 1 at a glance |
|---|---|
| Term | September 2025 term |
| Subject | Large Language Models |
| Course code | BSDA5004 |
| Questions | 24 |
| Marks | 50 |
| Duration | 180 min |
| MCQ | 16 |
| Numerical | 5 |
| MSQ | 3 |
| Official paper | Large Language Models 21 Dec 25 |
| Negative marking | No negative marking. |
| Updated |