Differentiate GenAI with high-quality data

To improve their text-to-image AI models, the company was looking for ways to rapidly generate high-quality human preference training data. However, creating and labeling this data internally would have diverted valuable resources and focus away from their core product development efforts. Data labeling required dedicated tooling and labeling teams to meet narrow timeframes given the company's pace of product development. Labelbox’s efficient and reliable data labeling platform and Boost Labeling Services was used to improve their text-to-image data quality, while delivering highly-skilled labelers along with project management experts that allowed them to meet their deadlines. Using Labelbox’s LLM human preference editor, the company was now able to perform reinforcement learning from human feedback (RLHF), which enables them to easily create preference data for training a reward model based on multiple outputs from a single model.