Use case
Complex reasoning
Generate and evaluate complex questions and responses to identify the best answer and provide logical reasoning.
Why Labelbox for complex reasoning
Increase data quality
Generate high-quality data by combining advanced tooling, humans, AI and on-demand services in a unified solution.
Accelerate time to value
Rapidly integrate data, create quality training data, and deploy models to production.
Access on-demand expertise
Highly-skilled labeling services, data science support, and industry insights available on-demand.
Collaborate in real-time
Enjoy direct access to internal and external labelers with real-time feedback on labels and quality via Labelbox platform.
Creating LLM training dataset for reasoning tasks
Use the Labelbox LLM multimodal editor to help internal and external teams create complex questions (prompts) and a corresponding solution that breaks down the logical steps required to arrive at the correct answer (response). Maximize quality with diverse prompt categories, differing difficulty levels, and details for each step of the response reasoning.
Monitoring performance of complex reasoning data
Monitor the performance, progress, and analytics of both internal and external labelers to ensure high-quality data creation. Enjoy quality assurance (QA) out the box to evaluate prompts and responses created to understand distribution, variety and quality of training data.
Accelerating development with on-demand services
Access an on-demand, highly skilled data labeling service with subject matter expertise to match your use cases. Collaborate with the workforce in real-time to maintain high data quality while keeping human labeling costs to a minimum using AI and automation techniques.
Customer spotlight
Dialpad, a leading AI-powered customer intelligence platform company used fine-tuning to build a powerful LLM over five years via five billion minutes of business conversations. The model offers out of the box capabilities to businesses to accurately summarize business calls, extract important insights and offer in-the-moment coaching to sellers and customer reps. Advanced discovery, curation, and annotation capabilities were crucial for building high-quality training datasets. Human evaluation was also critical to ensure quality outcomes before the system was turned live. Labelbox enabled this organization to accelerate the creation of the LLM by 75% through rich, integrated capabilities for data preparation and human evaluation.
