Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization

Research evaluates the potential of foundation models, specifically Vision-Language Models (VLMs), to address the escalating complexity of online content moderation policies. The study compares two primary methods for guiding these models: an instruction-driven approach based on policy precepts and an example-driven approach using precedents. This analysis utilizes ModerationBench, a new benchmark of 4,000 manually annotated online posts, to systematically compare these paradigms.

The consistent application of content moderation policies is critical for maintaining digital platform integrity and user safety. This research directly impacts the scalability and reliability of content moderation efforts by exploring advanced AI models as a potential solution, which could lead to more efficient and standardized enforcement across various online platforms.

The complexity of content moderation policies creates challenges for consistent operationalization.

Forward consideration, not a verified fact.