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LLM End Term: 31 August 2025, Set QDB1 (May 2025 term)

Question 1

+2 marksOne correct option

Given the input string:

moonlight

And the following vocabulary of subword tokens with their corresponding log-probabilities:

SubwordLog-Probability (base ee)
moon-0.4
light-1.0
moonlight-1.8
moo-0.3
nlight-0.6
n-2.0
li-0.5
ght-0.5

Using the Viterbi algorithm (as used in the SentencePiece tokenizer), determine the most probable tokenization of the input string. The probability of a tokenized sequence is the sum of the log-probabilities of the selected subwords. Only subwords from the vocabulary may be used.

  1. A
  2. B
  3. C
  4. D

Question 2

+2 marksOne correct option
  1. A
  2. B
  3. C
  4. D

Question 3

+3 marksOne or more correct options

Which of the following statements about subword tokenizers (e.g., Byte Pair Encoding, SentencePiece) is true?

Select all that apply.

  1. A

    They always split words into individual characters.

  2. B

    They help handle rare words by breaking them into smaller units.

  3. C

    They require the vocabulary to contain every possible word in the language.

  4. D

    They can reduce the overall vocabulary size compared to word-level tokenizers.

14 more questions in this paper

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More on the LLM End Term 31 Aug 2025 Set QDB1 paper

The IIT Madras BS Large Language Models (LLM) End Term paper sat on 31 Aug 2025, in the May 2025 term, set QDB1: 17 questions for 40 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.

FeatureLLM End Term 31 Aug 2025 Set QDB1 at a glance
TermMay 2025 term
SubjectLarge Language Models
Course codeBSDA5004
Questions17
Marks40
Duration180 min
MCQ5
MSQ5
Numerical7
Official paperIIT M DEGREE AN EXAM QDB3 31 Aug 2025
Negative markingNo negative marking.
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