Red Teaming

Red teaming, in the context of artificial intelligence, involves adversarial testing of AI models, particularly large language models (LLMs) and increasingly multimodal models, to identify vulnerabilities and biases. Current research focuses on automating this process using techniques like reinforcement learning, generative adversarial networks, and novel scoring functions to create diverse and effective adversarial prompts or inputs that expose model weaknesses. This rigorous evaluation is crucial for improving the safety, robustness, and ethical implications of AI systems, informing both model development and deployment strategies across various applications.