What is a primary motivation for using subword tokenization (like BPE or WordPiece) instead of word-level tokenization in transformers?
It increases vocabulary size exponentially
It completely eliminates the need for a tokenizer
It helps handle out-of-vocabulary words more effectively
It prevents the model from needing positional encoding
What is a key implication of scaling laws in large language models?
Training longer always leads to better performance
Larger models require less training data
Model size and dataset size must scale together to maintain optimal performance
None of these
Consider the Following Assertion and Reason pair :
Assertion : In Byte Pair Encoding (BPE), each merge operation adds exactly one new token to the vocabulary.
Reason : BPE helps reduce the size of the initial vocabulary by merging the most frequent tokens.
Both Assertion and Reason are true, and Reason is the correct explanation of Assertion.
Both Assertion and Reason are true, but Reason is not the correct explanation of Assertion.
Assertion is true, but Reason is false.
Assertion is false, but Reason is true.
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The IIT Madras BS Large Language Models (LLM) Quiz 2 paper sat on 3 Aug 2025, in the May 2025 term: 17 questions for 40 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 2 3 Aug 2025 at a glance |
|---|---|
| Term | May 2025 term |
| Subject | Large Language Models |
| Course code | BSDA5004 |
| Questions | 17 |
| Marks | 40 |
| Duration | 120 min |
| MCQ | 9 |
| MSQ | 3 |
| Numerical | 5 |
| Official paper | IIT M IMPROVEMENT AN EXAM QIA2 03 Aug 2025 |
| Negative marking | No negative marking. |
| Updated |
