Which platform is used to test the robustness of LLMs against adversarial attacks?

NeMo Guardrails is specifically designed to ensure that large language models (LLMs) are robust against adversarial attacks by providing a framework to define and enforce safety boundaries for model outputs. This includes mechanisms to validate inputs and prevent harmful or misleading outputs, making it an invaluable tool in evaluating and improving the resilience of LLMs against malicious attempts to distort model behavior.

In the context of testing LLMs, NeMo Guardrails offers features that help researchers and developers identify potential vulnerabilities and implement protective measures. This testing is crucial because it enables a deeper understanding of how these models can be exploited and how to mitigate risks, thus enhancing their reliability and safety in real-world applications.

The other options serve different purposes: NVIDIA Riva focuses on real-time applications of conversational AI; NVIDIA Merlin is aimed at optimizing recommendation systems; and LoRa refers to a low-power wide-area networking protocol, which is unrelated to LLM robustness testing. Each of these serves distinct roles in AI and machine learning but does not specifically target adversarial attacks on LLMs as NeMo Guardrails does.