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Question 24 - D-GAI-F-01 discussion

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What is the purpose of adversarial training in the lifecycle of a Large Language Model (LLM)?

A.
To make the model more resistant to attacks like prompt injections when it is deployed in production
Answers
A.
To make the model more resistant to attacks like prompt injections when it is deployed in production
B.
To feed the model a large volume of data from a wide variety of subjects
Answers
B.
To feed the model a large volume of data from a wide variety of subjects
C.
To customize the model for a specific task by feeding it task-specific content
Answers
C.
To customize the model for a specific task by feeding it task-specific content
D.
To randomize all the statistical weights of the neural network
Answers
D.
To randomize all the statistical weights of the neural network
Suggested answer: A

Explanation:

Adversarial training is a technique used to improve the robustness of AI models, including Large Language Models (LLMs), against various types of attacks. Here's a detailed explanation:

Definition: Adversarial training involves exposing the model to adversarial examples---inputs specifically designed to deceive the model during training.

Purpose: The main goal is to make the model more resistant to attacks, such as prompt injections or other malicious inputs, by improving its ability to recognize and handle these inputs appropriately.

Process: During training, the model is repeatedly exposed to slightly modified input data that is designed to exploit its vulnerabilities, allowing it to learn how to maintain performance and accuracy despite these perturbations.

Benefits: This method helps in enhancing the security and reliability of AI models when they are deployed in production environments, ensuring they can handle unexpected or adversarial situations better.

Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and Harnessing Adversarial Examples. arXiv preprint arXiv:1412.6572.

Kurakin, A., Goodfellow, I., & Bengio, S. (2017). Adversarial Machine Learning at Scale. arXiv preprint arXiv:1611.01236.

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asked 16/09/2024
Ben Pike
33 questions
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