Large Language Models · End Term · 10 May 2026 · January 2026 term

Question 18: Which of the following statements about fine-tuning stra…

Question 18

+2 marksOne or more correct options

Which of the following statements about fine-tuning strategies such as gradual unfreezing and multi-task learning in the context of the T5 model are correct?

Select all that apply.

A
In gradual unfreezing, layers are progressively unfrozen starting from the task-specific (top) layers toward the input (bottom) layers.
B
Multi-task learning in T5 typically improves performance uniformly across all tasks.
C
Multi-task pre-training (as used in T5) followed by task-specific fine-tuning often performs better than relying on multi-task learning alone.
D
Gradual unfreezing can help mitigate catastrophic forgetting of pre-trained representations during T5 fine-tuning.

Show answer

Correct answers

A
In gradual unfreezing, layers are progressively unfrozen starting from the task-specific (top) layers toward the input (bottom) layers.
B
Multi-task learning in T5 typically improves performance uniformly across all tasks.
D
Gradual unfreezing can help mitigate catastrophic forgetting of pre-trained representations during T5 fine-tuning.

See the whole paper Take it as a mock test

Question 18 of 20 in the IIT Madras BS Large Language Models (LLM) End Term paper sat on 10 May 2026, in the January 2026 term (Large Language Models 10 May 26). It carries 2 marks.

Question 18: Which of the following statements about fine-tuning stra…

Question 18

More questions from this paper