Question 2
Assume that we have a large corpus of text. The vocabulary constructed from the text contains 10000 words. Of these, 100 words occurred only once in the entire corpus of text. The parameters of the embedding layer and the output layer of the model are shared. Suppose we create a batch of 256 samples (each sample is a sentence from the corpus). None of these samples contains any of the 100 rare words. Suppose we pre-train the model for one iteration using the batch of samples, then:
it is certain that the embeddings of none of these 100 rare words will get updated.
there is a chance that the embeddings of all or some of these 100 rare words will get updated
the embeddings of all these 100 rare words will defintely get updated
None of these
Question 3
Suppose we use a pre-trained model for text generation with the given prompt “I am going to”. Which of the following decoding strategies can be used such that the pre-trained model generates same text completion each time it is executed
Beam search with beam size 4
Greedy approach
Top-K with k = 2
None of these
18 more questions in this paper
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More on the LLM End Term 22 Dec 2024 Set QDB4 paper
The IIT Madras BS Large Language Models (LLM) End Term paper sat on 22 Dec 2024, in the September 2024 term, set QDB4: 21 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 22 Dec 2024 Set QDB4 at a glance |
|---|---|
| Term | September 2024 term |
| Subject | Large Language Models |
| Course code | BSDA5004 |
| Questions | 21 |
| Marks | 50 |
| Duration | 180 min |
| Numerical | 6 |
| MCQ | 9 |
| MSQ | 5 |
| Written | 1 |
| Official paper | IIT M DEGREE AN EXAM QDB4 22 Dec 2024 |
| Negative marking | No negative marking. |
| Updated |