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.
Reference:
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.
