Use case
Preference ranking
Enable continuous improvement and optimization to ensure your GenAI models evolve and adapt to diverse user preferences over time with reinforcement learning.
Why Labelbox for preference ranking
Increase data quality for preference ranking
Use advanced tooling, on-demand experts, AI, and real-time quality metrics to generate high-quality data for preference ranking.
Accelerate time to value
Rapidly integrate data, create quality training data for preference ranking, and deploy models to production.
Access on-demand expertise
Harness highly-skilled AI trainers and industry-specific insights on-demand to perform in-depth preference ranking
Collaborate in real-time
Enjoy direct access to internal and external labelers with real-time feedback on preference ranking tasks via the Labelbox platform.
Understanding preference ranking
Preference ranking helps models adapt responses to changing user input and preferences, ensuring the conversation stays relevant and contextually appropriate by prioritizing the most relevant or useful response from a set of potential outputs.
Challenges in preference ranking
User preferences themselves are subjective, making it difficult to create a universal ranking system. This makes it difficult for models to consistently deliver optimal outputs for users. Without the right tools, analyzing and capturing feedback data can be slow and expensive.
Preference ranking with Labelbox
Use Labelbox to efficiently create diverse, high-quality datasets that improve the accuracy, fairness, and efficiency of ranking systems. The Labelbox platform streamlines the entire process, accelerating your time to value and maximizing your AI investments.
Customer spotlight
Labelbox's intuitive tooling coupled with post-training labeling services offered a collaborative environment where Speak's internal team, along with external data annotators, could work together seamlessly. Learn more about how Speak uses Labelbox to improving the quality and efficiency of their data labeling.
