NEWS · RESEARCH · #348
Expert-guided framework to generate context-specific LLM benchmarks
This arXiv preprint (v1) presents an end-to-end framework that combines expert input with synthetic data generation to produce context-specific benchmark datasets for large language models. It introduces a schema to capture goals, scope, and context, defines four measurement-validity criteria (coverage, diversity, content realism, stylistic realism), and reports quantitative evaluations plus a real-world case study showing expert-informed scaffolds improve dataset quality over existing methods.
KEY POINTS
- This arXiv preprint (v1) presents an end-to-end framework that combines expert input with synthetic data generation to produce context-specific benchmark datasets for large language models.
- It introduces a schema to capture goals, scope, and context, defines four measurement-validity criteria (coverage, diversity, content realism, stylistic realism), and reports quantitative evaluations plus a real-world case study showing expert-informed scaffolds improve dataset quality over existing methods.
- Offers a scalable way to build more valid, realistic, and diverse LLM benchmarks, addressing a key trade-off between expert-validated and synthetically generated datasets.
WHY IT MATTERS
Offers a scalable way to build more valid, realistic, and diverse LLM benchmarks, addressing a key trade-off between expert-validated and synthetically generated datasets.