SOURCE-LINKED INTELLIGENCE

A Framework for Generating Valid Context-Specific Benchmarks through Expert Guidance

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

This paper presents an end-to-end approach for generating context-specific large language model (LLM) benchmark datasets by combining expert input with synthetic data generation. Existing benchmark construction methods often trade off validity and scalability: datasets designed with domain experts can produce high-quality evaluations but are slow and costly to create, while synthetically generating data may scale efficiently but often results in unrealistic, redundant, or out-of-scope examples. To address this gap, we introduce a schema eliciting key information about the goals, scope, and con

Read original source ↗ Open in workspace

recordType

paper

region

Global