Question 134 NAT · 3.0 marks
Based on the above data answer the given subquestions.
What is the size of the vocabulary built by the Team C?
A published solution is not available for this question yet.
Large Language Models · Quiz 2 · Jan 2024
Large Language Models · Quiz 2 · Jan 2024
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Question 135 MSQ · 4.0 marks
Based on the above data answer the given subquestions.
Choose the correct statements about the number of parameters in the model (excluding the
embedding and output layer parameters)
Team A model has less number of parameters than the team B
Team B model has less number of parameters than the team C
Team C model has more number of parameters than the team A
All models have the same number of parameters
A published solution is not available for this question yet.
Question 136 MSQ · 4.0 marks
Based on the above data answer the given subquestions.
Assume that the word “acrophobia” is not present in the vocabulary, then which of the following
tokenizer(s) is(are) guaranteed to tokenize this word into sub-words units (i.e., it does not return
[unk] token)
The tokenizer used by the team A
the tokenizer used by the team B
the tokenizer used by the team C
None of the given options
A published solution is not available for this question yet.
Question 137 MCQ · 3.0 marks
Based on the above data answer the given subquestions.
Assume that team A and team B pre-trained the model for 1 million steps. Suppose they want to
fine-tune the model for sentiment classification tasks (where the dataset contains sentences, digits
and emojis). Choose the correct statement
Team A can use the tokenizer from Team B and fine-tune the model and vice
versa
We can not interchange both teams’ tokenizers while fine-tuning.
A published solution is not available for this question yet.
Question 138 MSQ · 4.0 marks
Suppose we have two decoder-only models, namely Model A and Model B. Assume we have two
datasets, one for unsupervised pre-training (Web-Text) and the other for supervised training (text
summarization). Model A was trained directly on the training set of the supervised dataset. Model
B was trained on the pre-training dataset using the CLM objective. Suppose we want to test the
performance of both models on the test set of summarization task. Then choose the correct
statements
Model A can directly be used on the test set
It is possible to use Model B directly on the test set without fine-tuning on the
training set
Model B **should be** fine-tuned first on the training set and then use the test
set.
A published solution is not available for this question yet.
Question 139 MSQ · 4.0 marks
[v] of science is [w] contradictory attitudes [x] counterintuitive they may be [y]
how deep truths are winnowed [z]
[a] of science is [b] contradictory attitudes [c] counterintuitive they may be [d]
how deep truths are winnowed [z]
[v] of science is [w] contradictory attitudes [x] counterintuitive they may be [y]
how deep truths are winnowed
[a] of science is [b] contradictory attitudes [c] counterintuitive they may be [d]
how deep truths are winnowed
[w] contradictory attitudes [v] of science is [x] counterintuitive they may be [y]
how deep truths are winnowed [z]
[v] of science is [v] contradictory attitudes [x] counterintuitive they may be [x]
how deep truths are winnowed [y]
A published solution is not available for this question yet.
Question 140 MSQ · 3.0 marks
Which of the following models uses a form of denoising objective for pre-training?
BERT
GPT-1 and GPT-2
BART
T5
A published solution is not available for this question yet.
Question 141 MSQ · 4.0 marks
Consider training an encoder-decoder model (say, T5 baseline) on a pre-training dataset that
contains 1 Billion tokens. Let the batch size be 64 × 128, that is 2\(^{13}\) tokens. Suppose the model is
trained for 2\(^{15}\) steps by varying the size of the data (all other aspects are fixed)
A. Train the model only on the first one million tokens, referred to as Model A
B. Train the model on the entire dataset, referred to as Model B
Based on the above data answer the given subquestions.
Which of the following statements is (are) correct under this setup?
Training loss of Model A will be less than training loss of Model B
Training loss of Model B will be less than training loss of Model A
Test loss of Model B will likely be less than test loss of Model A
Test loss of Model A will likely be less than test loss of Model B
A published solution is not available for this question yet.
Question 142 MSQ · 4.0 marks
Consider training an encoder-decoder model (say, T5 baseline) on a pre-training dataset that
contains 1 Billion tokens. Let the batch size be 64 × 128, that is 2\(^{13}\) tokens. Suppose the model is
trained for 2\(^{15}\) steps by varying the size of the data (all other aspects are fixed)
A. Train the model only on the first one million tokens, referred to as Model A
B. Train the model on the entire dataset, referred to as Model B
Based on the above data answer the given subquestions.
Which of the following modifications will improve the performance of both models?
Increasing the training steps helps Model B more than Model A in terms of
generalization performance
Increasing the training steps helps both Model A and Model B in terms of
generalization performance
None of the given modifications helps the model to improve their
generalization performance
A published solution is not available for this question yet.
Question 143 MSQ · 5.0 marks
Consider a summarization task. Assume the prefix is “Summarize the following:” and the input
sentence is “There are a lot of videos on YouTube etc. that give the appearance of education, but if
you look closely they are really just entertainment. This is very convenient for everyone involved:
the people watching enjoy thinking they are learning (but they are just having fun)” and the model
is supposed to summarize it.Then which of the following architectures **could be used** for this task
A decoder-only model with a causal mask
A decoder-only model with a prefix mask
An encoder-only model with a fully visible mask
Encoder-Decoder model with fully visible mask in the encoder and causal mask
in the decoder
A published solution is not available for this question yet.
Question 144 MSQ · 4.0 marks
Suppose we are given a Model A with N layers and the dataset with D tokens. Choose the correct
statements according to the scaling law.
Increasing the model size requires a proportionate increase in the dataset size
to reduce the test loss
Increasing the model size requires a proportionate increase in the dataset size
to reduce the training loss
Increasing the model size requires us to double the dataset size to reduce the
test loss
Increasing the model size requires us to double the dataset size to reduce the
train loss
A published solution is not available for this question yet.
Question 145 MCQ · 3.0 marks
Which of the following components in the data pre-processing pipeline removes pages that
contain bad words?
Language Identification
Exact Deduplication
Fuzzy Deduplication
ML classifiers for quality filtering
Simple heuristics to detect toxic contents
A published solution is not available for this question yet.
Question 146 MCQ · 3.0 marks
Suppose a team wants to build a dataset for a low-resource language. They took all Wikipedia
dumps for that language and found that the dataset size was insufficient. Then the statement that
using years of Common Crawl Snapshots would be helpful to increase the size of the dataset is
TRUE
FALSE
A published solution is not available for this question yet.
Question 147 MCQ · 2.0 marks
The statement that the presence of significant duplicate contents in the pre-training dataset
degrades the performance of Large Language Models is
TRUE
FALSE
**DL(CV)**
**Section Id :** 64065353063
**Section Number :** 10
**Section type :** Online
**Mandatory or Optional :** Mandatory
**Number of Questions :** 23
**Number of Questions to be attempted :** 23
**Section Marks :** 50
**Display Number Panel :** Yes
**Section Negative Marks :** 0
**Group All Questions :** No
**Enable Mark as Answered Mark for Review and**
Yes
**Clear Response :**
**Maximum Instruction Time :** 0
A published solution is not available for this question yet.