Yes, guardrails are compatible with multimodal LLMs, which are designed to handle multiple types of input and output, such as text, images, audio, and video. Guardrails can be tailored to address the unique challenges posed by each modality. For example, in a multimodal system that processes both text and images, guardrails can detect harmful or biased content in both formats, ensuring that any text output remains appropriate while filtering out explicit or offensive visuals.
Guardrails for multimodal LLMs work by applying separate or integrated safety layers that account for the different ways in which each modality can impact the system's outputs. For instance, textual guardrails may focus on detecting harmful language, while image guardrails can identify visual content that violates ethical guidelines. The integration of these safety layers allows for seamless moderation across all content types in real-time.
In practice, implementing multimodal guardrails requires coordination between the various safety systems that govern different modalities. Developers need to ensure that each modality’s guardrails are compatible and that the overall system responds appropriately when a violation occurs in any single modality. This might involve using specialized filters and machine learning models to address the unique risks associated with each type of data while ensuring the system functions cohesively as a whole.