Benchmark Harmonization and Model Similarity Analysis

Project Overview

At the FORESEER Lab (University of Michigan), under the supervision of Prof. Qiaozhu Mei, I’m working on a critical challenge in AI evaluation: harmonizing diverse benchmarks to enable principled cross-benchmark comparison and understanding model relationships across different evaluation frameworks.

Key Contributions

• Unified three major benchmarks: LiveBench, HELM, and LMSYS Arena into a coherent evaluation framework
• Standardized model identifiers across different evaluation frameworks for consistent comparison
• Deduplicated variants and retained latest versions per model–objective pair for clean analysis
• Enabled principled cross-benchmark comparison for the first time at this scale
• Constructed comprehensive similarity maps using multidimensional scaling (MDS) techniques
• Implemented centrality analysis to identify key models and relationships in the evaluation space
• Reported rigorous fit metrics including raw stress and Stress-1 measurements for validation
• Verified stability under injected noise and sub-sampling conditions for robustness
• Diagnosed sources of misalignment between different evaluation approaches
• Proposed compact task subsets that preserve similarity structure while reducing computational cost

Technical Skills Demonstrated

• Cross-platform data integration and model ID standardization across diverse systems
• Variant deduplication algorithms for large-scale dataset cleaning and processing
• Multi-benchmark data alignment and validation techniques for complex datasets
• Multidimensional scaling implementation and optimization for high-dimensional analysis
• Statistical stress metric calculation and interpretation for dimensionality reduction
• Noise injection protocols and stability testing for robust experimental validation
• Interactive data visualization development for model similarity and relationships
• Meta-analysis methodology for evaluation framework comparison and optimization

Impact and Applications

This research addresses fundamental challenges in AI evaluation and provides practical benefits:

• Reduces evaluation costs while maintaining assessment quality through efficient subset selection
• Enables meta-analysis across previously incompatible benchmarks for broader insights
• Provides insights into model relationships and evaluation redundancies across the field
• Guides future benchmark development through systematic analysis of existing frameworks
• Supports model selection and comparison through comprehensive similarity analysis
• Improves evaluation standardization across the AI research community

Research Significance

This work contributes to making AI evaluation more efficient, standardized, and scientifically rigorous across the field. The methodological innovations in cross-benchmark standardization and robust statistical validation of similarity structures provide a foundation for future evaluation research and practical frameworks for evaluation cost reduction in both academic and industrial settings.