Fine-Grained Benchmark Generation for Comprehensive Evaluation of Foundation Models
A new framework for automated benchmark generation has been introduced to improve the evaluation of foundation models. This framework produces benchmarks with comprehensive coverage and rich metadata, addressing limitations of existing benchmarks. The initial results show a significant reduction in ground-truth error rates compared to previous benchmarks.
- ▪The framework generates evaluation problems based on reference materials like textbooks.
- ▪It employs a multi-agent architecture for problem generation and a solution-graph-driven strategy.
- ▪Three benchmarks in Machine Learning, Corporate Finance, and Personal Finance have been generated using this framework.
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Computer Science > Machine Learning arXiv:2605.18824 (cs) [Submitted on 12 May 2026] Title:Fine-Grained Benchmark Generation for Comprehensive Evaluation of Foundation Models Authors:Mohammed Saidul Islam, Negin Baghbanzadeh, Farnaz Kohankhaki, Afshin Cheraghi, Ali Kore, Shayaan Mehdi, Elham Dolatabadi, Arash Afkanpour View a PDF of the paper titled Fine-Grained Benchmark Generation for Comprehensive Evaluation of Foundation Models, by Mohammed Saidul Islam and 7 other authors View PDF HTML (experimental) Abstract:Evaluation of foundation models often rely on aggregate scores from benchmarks that lack comprehensive coverage and metadata for a fine-grained evaluation. We introduce a framework for automated benchmark generation.
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