Fine-Grained Benchmark Generation for Comprehensive Evaluation of Foundation Models
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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
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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. Our framework generates evaluation problems grounded in reference material, such as textbooks, producing benchmarks with broad coverage, rich metadata, and robustness to contamination. The pipeline employs a multi-agent architecture for problem generation and a solution-graph-driven strategy that significantly improves the reliability of ground truth solutions. Using the framework, we generate three benchmarks in Machine Learning, Corporate Finance, and Personal Finance. Expert review finds a significantly lower ground-truth error rate than previous benchmarks such as MMLU and GSM8K. Evaluation of 12 commercial and open-source models shows that our benchmarks achieve near-uniform competency coverage and surface performance differences across models that existing benchmarks fail to capture. We will open-source the framework and our curated benchmarks soon.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.18824 [cs.LG] |
| (or arXiv:2605.18824v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.18824
arXiv-issued DOI via DataCite
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Submission history
From: Mohammed Saidul Islam [view email][v1] Tue, 12 May 2026 17:01:58 UTC (14,110 KB)
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