MicroProbe: Efficient Reliability Assessment for Foundation Models with Minimal Data

TL;DR

Foundation-model reliability assessment typically requires thousands of examples, driving cost and latency. MicroProbe introduces strategic probe selection to achieve comprehensive reliability coverage with only 100 probes, exploiting diversity across five reliability dimensions and uncertainty-aware scoring. The approach combines adaptive weighting and information-theoretic justification, delivering 23.5% improvement over random baselines with 99.9% statistical power and strong expert validation, while reducing assessment cost by about 90%. Extensive cross-model and cross-domain validation demonstrates practical applicability for responsible AI deployment, supported by reproducible workflows and robust statistical analyses.

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.

Figures (7)

• Figure 1: Performance comparison across models and methods. MicroProbe consistently outperforms all baseline approaches across multiple model architectures.
• Figure 2: Statistical validation with distribution comparison (left) and 95% confidence intervals (right). Results show large effect size (Cohen's d = 1.21) with exceptional statistical significance (p < 0.001) and 99.9% statistical power.
• Figure 3: Cross-domain performance analysis across healthcare, finance, and legal applications. MicroProbe demonstrates consistent advantages across diverse domain-specific reliability assessments.
• Figure 4: Expert validation results showing significantly higher ratings for strategic probe selection across all evaluation criteria. Three AI safety researchers provided independent assessments with strong inter-rater reliability (/alpha = 0.92).
• Figure 5: Efficiency comparison showing 90% reduction in assessment time, computational cost, and required samples. MicroProbe achieves substantial resource savings while maintaining assessment quality and statistical rigor.