Sarah Kim
Head of Quality ·
Multi-modal AI models require training data that spans text, images, video, and sometimes 3D point clouds. Annotating across these modalities introduces unique challenges that most teams underestimate.
Cross-modal consistency is the biggest challenge. When annotating image-text pairs, the text descriptions must precisely match the visual content. Our annotators use side-by-side interfaces that make alignment errors immediately visible.
For video annotation, temporal consistency is critical. Objects must maintain consistent identities across frames, and action labels must align with the exact moments they occur. We use specialized tracking tools that reduce annotation time while improving accuracy.
The data labeling market is projected to reach $17B by 2030, with 60% of enterprises outsourcing annotation. A comprehensive guide to evaluating and selecting the right data annotation partner.
34% of multimodal annotations had sync errors in one major project. Explore the challenges, best practices, and quality frameworks for annotating text, image, video, and 3D data for generative AI.
A practical comparison of RLHF and DPO for aligning large language models — covering data requirements, cost, quality trade-offs, and when to use each approach.
