Human preference data is key to boosting performance in frontier models and AI systems, yet collecting high-quality datasets at scale can be challenging. SuperAnnotate streamlines large-scale dataset creation with robust quality assurance and project management, enabling faster, better preference datasets.
Training reward models or using DPO requires large-scale, high-quality preference datasets. However, building these datasets comes with significant challenges that can hinder your ability to collect data at scale without sacrificing quality:
Even a small number of low-quality data points in a large dataset can massively degrade model quality.
The overhead of managing a large crowd or private workforce can easily delay projects.
More and more companies are moving to hybrid human-synthetic data, but combining AI-generated data with human validation can be complex.
Models are increasingly multimodal, which challenges data collection interfaces and crowd knowledge.
SuperAnnotate addresses these challenges by streamlining the entire preference data collection process. From workforce management to hybrid synthetic workflows, our platform is designed to scale with your needs while ensuring data quality and efficiency.
Manage large annotation teams efficiently with SuperAnnotate’s centralized project and workforce management tools. Track progress in real-time, assign tasks based on skills and regions, and ensure consistent application of guidelines across all annotators.
Set up complex workflows with multiple manual or automated touchpoints to refine annotations at every stage.
Create hybrid datasets by leveraging a model-in-the-loop approach and create automation that automatically validates code SFT by running it against databases.
Whether evaluating multimodal models, undertaking fine-grained RLHF, or collecting preference over multiple categories, SuperAnnotates UI can be adapted to your task.