{"slug": "a-framework-for-generating-valid-context-specific-benchmarks-through-expert", "title": "A Framework for Generating Valid Context-Specific Benchmarks through Expert Guidance", "summary": "A new arXiv paper (2609.16592v1) introduces an end-to-end framework for generating context-specific large language model benchmark datasets by combining expert input with synthetic data generation. The framework uses a schema capturing an evaluation task's goals, scope, and context to guide synthetic data generation, and defines four measurement-validity criteria for dataset quality: coverage, diversity, content realism, and stylistic realism. Quantitative evaluations and a real-world case study with domain experts show the expert-informed scaffolds improve benchmark data quality over existing methods while preserving validity, with the paper also analyzing which schema information to prioritize under resource constraints.", "body_md": "arXiv:2609.16592v1 Announce Type: new \nAbstract: 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 context of an evaluation task, and use this information to guide synthetic data generation. We further define four criteria grounded in measurement validity for assessing dataset quality: coverage, diversity, content realism, and stylistic realism. Using these criteria, we show how expert-informed scaffolds can guide synthetic data generation toward more valid benchmarks. Through quantitative evaluations and a real-world case study with domain experts, we demonstrate that our approach improves benchmark data quality over existing methods while preserving validity. We additionally analyze how different types of schema information affect different dataset quality criteria, and provide practical guidance on which information to prioritize collecting under resource constraints.", "url": "https://wpnews.pro/news/a-framework-for-generating-valid-context-specific-benchmarks-through-expert", "canonical_source": "https://www.machinebrief.com/news/a-framework-for-generating-valid-context-specific-benchmarks-rdwu", "published_at": "2026-09-16 04:00:00+00:00", "updated_at": "2026-09-16 04:36:03.702477+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "ai-safety"], "entities": ["arXiv", "2609.16592v1"], "alternates": {"html": "https://wpnews.pro/news/a-framework-for-generating-valid-context-specific-benchmarks-through-expert", "markdown": "https://wpnews.pro/news/a-framework-for-generating-valid-context-specific-benchmarks-through-expert.md", "text": "https://wpnews.pro/news/a-framework-for-generating-valid-context-specific-benchmarks-through-expert.txt", "jsonld": "https://wpnews.pro/news/a-framework-for-generating-valid-context-specific-benchmarks-through-expert.jsonld"}}