MicroProbe: Efficient Reliability Assessment for Foundation Models with Minimal Data

• URL: http://arxiv.org/abs/2512.20630v1
• Date: Sun, 30 Nov 2025 13:01:57 GMT
• Title: MicroProbe: Efficient Reliability Assessment for Foundation Models with Minimal Data
• Authors: Aayam Bansal, Ishaan Gangwani,
• Abstract summary: microprobe achieves comprehensive reliability assessment using only 100 strategically selected probe examples.<n>We demonstrate that microprobe achieves 23.5% higher composite reliability scores compared to random sampling baselines.<n> microprobe completes reliability assessment with 99.9% statistical power while representing a 90% reduction in assessment cost and maintaining 95% of traditional method coverage.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation model reliability assessment typically requires thousands of evaluation examples, making it computationally expensive and time-consuming for real-world deployment. We introduce microprobe, a novel approach that achieves comprehensive reliability assessment using only 100 strategically selected probe examples. Our method combines strategic prompt diversity across five key reliability dimensions with advanced uncertainty quantification and adaptive weighting to efficiently detect potential failure modes. Through extensive empirical evaluation on multiple language models (GPT-2 variants, GPT-2 Medium, GPT-2 Large) and cross-domain validation (healthcare, finance, legal), we demonstrate that microprobe achieves 23.5% higher composite reliability scores compared to random sampling baselines, with exceptional statistical significance (p < 0.001, Cohen's d = 1.21). Expert validation by three AI safety researchers confirms the effectiveness of our strategic selection, rating our approach 4.14/5.0 versus 3.14/5.0 for random selection. microprobe completes reliability assessment with 99.9% statistical power while representing a 90% reduction in assessment cost and maintaining 95% of traditional method coverage. Our approach addresses a critical gap in efficient model evaluation for responsible AI deployment.

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