Red-Teaming AI Models
Red-teaming is an evaluation method that identifies vulnerabilities in models, potentially leading to undesirable behaviors such as the generation of offensive content or the revelation of personal information. Strategies such as Generative Discriminator Guided Sequence Generation (GeDi) and Plug and Play Language Models (PPLM) have been developed to steer models away from such outcomes.
The practice of red-teaming LLMs typically involves crafting prompts that trigger harmful text generation, revealing model limitations that could facilitate violence or other illegal activities. It requires creative thinking and can be resource-intensive, making it a challenging yet crucial aspect of LLM development.
Red-teaming is still an emerging research area that needs continual adaptation of methods. Best practices include simulating scenarios with potential bad consequences, like power-seeking behavior or online purchases via an API.
Open-source datasets for red-teaming are available from organizations such as Anthropic and AI2. Anthropic’s red-team dataset can be downloaded from https://huggingface.co/datasets/Anthropic/hh-rlhf/tree/main/red-team-attempts. AI2’s red-team datasets can be downloaded from https://huggingface.co/datasets/allenai/real-toxicity-prompts
Past studies have shown that few-shot-prompted LMs are not harder to red-team than plain LMs, and there is a tradeoff between a model’s helpfulness and harmlessness. Future directions for red-teaming include creating datasets for code generation attacks and designing strategies for critical threat scenarios. Companies like Google, OpenAI, and Microsoft have developed several efforts related to Red Teaming AI models. For example, OpenAI created the AI Red Teaming Network (https://openai.com/blog/red-teaming-network) to work with individual experts, research institutions, and civil society organizations to find vulnerabilities in AI implementations.