Question 143 MCQ · 0.0 marks
Useful Data has been mentioned above
This data attachment is just for a reference & not for an evaluation.
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Large Language Models · End Term · Jan 2024 · AN
Large Language Models · End Term · Jan 2024 AN
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Question 144 MCQ · 2.0 marks
GPT stands for
Generative Pre-trained Transformer
Generative Pre-trained Text
Generalized Pre-trained Transformer
Generative Pre-trained Transfer-text
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Question 145 MCQ · 4.0 marks
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Question 146 MSQ · 3.0 marks
Suppose we pre-train a model using a text-to-text framework. A user wants to fine-tune the pre-
trained model on a regression task where the inputs are sentences but the labels are real-valued.
The label values range from 0.1 to 2 with a step size of 0.1. Given these, choose the correct
statements.
The objective function of the pre-trained model should be modified during
fine-tuning
The objective function of the pre-trained model need not be modified during
fine-tuning
Text-to-text framework cannot be used for any regression problem
We need to add a layer, on top of the output layer, that outputs a real value
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Question 147 NAT · 3.0 marks
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Question 148 SHORT_TEXT · 4.0 marks
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Question 149 MSQ · 3.0 marks
Suppose a GPT model is pre-trained using the Causal Language Modelling (CLM) objective and
fine-tuned on a text summarization task. The context length T of the model was kept at 2048
during both pre-training and fine-tuning. Assume that the model uses Absolute Position
Embedding (APE) with **learnable** parameters.
Based on the above data, answer the given subquestions.
A user runs inference on the model N times with an input text that contains 712 tokens (including
the special [start] token), then which of the following could be the possible length of summarized
text
256
16
1024
512
2048
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Question 150 MSQ · 3.0 marks
Suppose a GPT model is pre-trained using the Causal Language Modelling (CLM) objective and
fine-tuned on a text summarization task. The context length T of the model was kept at 2048
during both pre-training and fine-tuning. Assume that the model uses Absolute Position
Embedding (APE) with **learnable** parameters.
Based on the above data, answer the given subquestions.
A user runs inference on the model N times with an input text that contains 712 tokens (including
the special [start] token). However, the user observed that in each run the model generated the
exact same summary. Based on this observation, select the decoding strategy that the model
might be using
Greedy decoding strategy
Beam search with beam size K > 2
Top-k sampling with k > 3
Top-p sampling with p = 0.25
Greedy decoding by varying the temperature parameter T in the softmax
function for each run
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Question 151 NAT · 4.0 marks
Based on the above data, answer the given subquestions.
Suppose the number of encoder layers N = 3. What is the total number of learnable parameters,
including the parameters of the input and position embedding layers, in the model? Enter the final
answer in thousands. For example, if the answer is 12,238, then enter 12 as the answer.
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Question 152 NAT · 3.0 marks
Based on the above data, answer the given subquestions.
Suppose we increase the context length from T = 256 to T = 512 keeping number of layers same (N
= 3). How many parameters does the model need to learn **additionally** compared to the previous
setting?
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Question 153 NAT · 4.0 marks
Based on the above data, answer the given subquestions.
Compute the representation for the word “often” and enter the sum of the elements in it.
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Question 154 NAT · 4.0 marks
Based on the above data, answer the given subquestions.
Suppose the input sentence is “coffee i drink often“. What is the attention score between the word
“coffee” with “often”?
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Question 155 NAT · 3.0 marks
Based on the above data, answer the given subquestions.
What is the input sequence length that would have generated this matrix?
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Question 156 MSQ · 4.0 marks
Based on the above data, answer the given subquestions.
(8,8)
(32,8)
(32,32)
(64,64)
(8,32)
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Question 157 NAT · 4.0 marks
Based on the above data, answer the given subquestions.
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Question 158 MSQ · 2.0 marks
Multi-Query attention
Grouped Query attention
Paged attention
None of these
**DL(CV)**
**Section Id :** 64065357859
**Section Number :** 8
**Section type :** Online
**Mandatory or Optional :** Mandatory
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