AI Red Teams for Adversarial Training: How to Make ChatGPT and LLMs Adversarially Robust

The text explores the challenges and strategies of ensuring AI language models behave safely and do not promote violence, emphasizing the complexities involved in training these models. It illustrates how language models can generate both benign and violent solutions to scenarios, underscoring the difficulty of detecting subtle or creative forms of violence. The text discusses the limitations of traditional data labeling and introduces the concept of "AI Red Teams," which actively engage with models to identify and address failures by generating new adversarial examples. This iterative process aims to make models more robust against adversarial inputs, as evidenced by efforts like those with Redwood Research to create a robust injury detection classifier. The text also highlights real-world applications, such as social media platforms' need for robust toxicity detectors, and references historical examples like Microsoft's Tay chatbot to underline the importance of adversarial training. It concludes by reflecting on the potential dangers of future intelligent models and the necessity of developing interactive and generative approaches to AI training, using language models as a test bed for future advancements.

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