Composite Benchmarks
Multi-dimensional evaluation frameworks combining multiple benchmarks for comprehensive AI assessment
What are Composite Benchmarks?
Composite benchmarks combine multiple individual benchmarks into unified evaluation frameworks that provide holistic assessments of AI system capabilities. Rather than relying on single metrics, they aggregate performance across diverse domains like reasoning, knowledge, safety, and robustness to create comprehensive capability profiles.
Composite Score Calculator
Results
Composite performance across 5 benchmarks using equal weighting
Composite Benchmark Design
Core Components
Benchmark Selection
Choose complementary benchmarks covering different capability dimensions
Score Aggregation
Weighted combination methods accounting for benchmark reliability
Correlation Analysis
Statistical validation of benchmark independence and complementarity
Weighting Strategies
Equal Weighting
All benchmarks contribute equally to final score
Domain-Based
Weights based on domain importance and use case requirements
Performance-Based
Dynamic weights based on benchmark reliability and variance
Popular Composite Benchmarks
MMLU+ Extended
Extended Massive Multitask Language Understanding with additional safety and robustness evaluations
57 academic subjects + safety + bias detection
70% knowledge, 20% safety, 10% robustness
General-purpose AI evaluation
BigBench Composite
Curated subset of BigBench tasks covering diverse reasoning and knowledge domains
42 selected tasks from 200+ available
Task-difficulty adjusted weighting
Research and model comparison
HELM Aggregate
Holistic Evaluation of Language Models with aggregated metrics across multiple scenarios
16 scenarios × 7 metrics = 112 evaluations
Scenario importance and metric reliability
Comprehensive model assessment
Implementation Examples
Composite Benchmark Framework
Statistical Aggregation Methods
Benchmark Correlation Analysis
Best Practices
✅ Recommended Approaches
Diverse Benchmark Selection
Include benchmarks covering different capabilities, domains, and evaluation methodologies
Statistical Validation
Validate benchmark correlations and ensure statistical significance of composite scores
Transparent Weighting
Document weighting rationale and provide multiple aggregation views
Regular Updates
Update benchmark composition as new evaluations become available
❌ Common Pitfalls
Highly Correlated Benchmarks
Avoid including multiple benchmarks that measure the same underlying capability
Arbitrary Weighting
Don't use weights without theoretical or empirical justification
Gaming Individual Components
Prevent optimization targeting specific benchmarks rather than general capability
Ignoring Confidence Intervals
Always report uncertainty and avoid over-interpreting small score differences