Content moderation demands meticulous effort, sensitivity, a profound understanding of context, as well as quick adaptation to new use cases, making it both time consuming and challenging. Traditionally, the burden of this task has fallen on human moderators sifting through large amounts of content to filter out toxic and harmful material, supported by smaller vertical-specific machine learning models. The process is inherently slow and can lead to mental stress on human moderators.

We're exploring the use of LLMs to address these challenges. Our large language models like GPT‑4 can understand and generate natural language, making them applicable to content moderation. The models can make moderation judgments based on policy guidelines provided to them.

With this system, the process of developing and customizing content policies is trimmed down from months to hours.

This iterative process yields refined content policies that are translated into classifiers, enabling the deployment of the policy and content moderation at scale.