We’re introducing a new model built on GPT-4o that is more accurate at detecting harmful text and images, enabling developers to build more robust moderation systems.
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We use GPT-4 for content policy development and content moderation decisions, enabling more consistent labeling, a faster feedback loop for policy refinement, and less involvement from human moderators.
We are introducing a new and improved content moderation tool. The Moderation endpoint improves upon our previous content filter, and is available for free today to OpenAI API developers.
arXiv:2604. 06205v2 Announce Type: replace-cross Abstract: The growth of online platforms and user content requires strong content moderation systems that can handle complex inputs from various media types.
The paper presents a scalable approach to harmful content moderation on social media by leveraging large language models (LLMs) for few-shot, in-context learning. Experiments across multiple LLMs show that this method outperforms proprietary baselines such as Perspective and OpenAI Moderation, as well as prior few-shot learning techniques, in detecting harmful content. The study also explores the addition of visual cues like video thumbnails to assess multimodal improvements, highlighting the advantages of LLM-based moderation for dynamic and large-scale content filtering.
Discover how SafetyKit leverages OpenAI GPT-5 to enhance content moderation, enforce compliance, and outpace legacy safety systems with greater accuracy .
arXiv:2607. 24898v1 Announce Type: cross Abstract: State-of-the-art toxicity detectors for text-to-image generation adopt a one-size-fits-all approach: a single universal model applying fixed safety guidelines to all users.