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

Question 6: Select the statements that correctly distinguish fine-tun…

Question 6

+3 marksOne or more correct options

Select the statements that correctly distinguish fine-tuning from zero-shot prompting for GPT-style models.

Select all that apply.

A
In Fine-Tuning, the weights of the pre-trained model (and often a new specific head) are updated using backpropagation on a labeled dataset.
B
In Zero-Shot Prompting, the model weights are frozen (not updated); the task is formulated purely through the design of the input text (prompt).
C
Zero-Shot Prompting generally achieves higher accuracy than Fine-Tuning when abundant labeled data is available.
D
Fine-Tuning typically requires a task-specific output layer (like a linear classifier), whereas Zero-Shot Prompting relies on the model generating text directly.

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Correct answers

A
In Fine-Tuning, the weights of the pre-trained model (and often a new specific head) are updated using backpropagation on a labeled dataset.
B
In Zero-Shot Prompting, the model weights are frozen (not updated); the task is formulated purely through the design of the input text (prompt).
D
Fine-Tuning typically requires a task-specific output layer (like a linear classifier), whereas Zero-Shot Prompting relies on the model generating text directly.

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Question 6 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 3 marks.

Question 6: Select the statements that correctly distinguish fine-tun…

Question 6

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