multimodal

Multimodal data curation, RLHF and preference ranking

Scale human feedback collection for model alignment. Filter massive datasets, generate comparison pairs, and capture nuanced preferences across video, audio, and text modalities for RLHF training.

Intelligent Data Curation

Filter billions of assets using metadata searches and quality metrics in Encord Index. Create targeted collections from search results. Build datasets optimized for preference learning scenarios.

Automated Comparison Generation

Generate captions using VLMs, then create semantic variations through rephrasing agents. Set up pairwise comparisons across modalities. Configure multiple-choice scoring and preference ranking interfaces.

Human Preference Capture

Present side-by-side asset comparisons for annotator evaluation. Collect preference rankings with detailed reasoning. Export structured feedback data for RLHF model training pipelines.

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