Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·26d ago·5 min read
A Framework for Generating Valid Context-Specific Benchmarks through Expert Guidance
TL;DR
A new framework combines expert input with synthetic data generation to create context-specific benchmarks for large language models (LLMs). This approach enhances dataset quality by ensuring coverage, diversity, and realism while maintaining scalability.
✦ Why It Matters
Implement expert-guided synthetic data generation to enhance your LLM benchmarks today.
Key Takeaways
- Utilize expert input to guide synthetic data generation.
- Focus on coverage and diversity in benchmark datasets.
- Assess dataset quality using defined criteria.
- Prioritize collecting specific schema information under constraints.
How It Works
The framework elicits key information from experts regarding the goals and context of evaluation tasks. This information guides the synthetic data generation process, ensuring that the generated datasets are relevant and realistic.
By defining quality criteria, the framework allows for systematic evaluation and improvement of benchmark datasets.