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Question 11 - D-GAI-F-01 discussion

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What is the primary purpose of fine-tuning in the lifecycle of a Large Language Model (LLM)?

A.
To randomize all the statistical weights of the neural network
Answers
A.
To randomize all the statistical weights of the neural network
B.
To customize the model for a specific task by feeding it task-specific content
Answers
B.
To customize the model for a specific task by feeding it task-specific content
C.
To feed the model a large volume of data from a wide variety of subjects
Answers
C.
To feed the model a large volume of data from a wide variety of subjects
D.
To put text into a prompt to interact with the cloud-based Al system
Answers
D.
To put text into a prompt to interact with the cloud-based Al system
Suggested answer: B

Explanation:

Definition of Fine-Tuning: Fine-tuning is a process in which a pretrained model is further trained on a smaller, task-specific dataset. This helps the model adapt to particular tasks or domains, improving its performance in those areas.

Purpose: The primary purpose is to refine the model's parameters so that it performs optimally on the specific content it will encounter in real-world applications. This makes the model more accurate and efficient for the given task.

Example: For instance, a general language model can be fine-tuned on legal documents to create a specialized model for legal text analysis, improving its ability to understand and generate text in that specific context.

asked 16/09/2024
Ahmed Otmani Amaoui
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