MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures
MixEval-X builds real-world multi-modal AI benchmarks by mixing existing tests with actual web queries
MixEval-X builds real-world multi-modal AI benchmarks by mixing existing tests with actual web queries
Tackles AI testing bias by mapping real user tasks to standardized evaluation metrics
Original Problem 🔍:
AI evaluations lack consistency across communities and suffer from query, grading, and generalization biases, hindering accurate model assessment and development.
Solution in this Paper 🛠️:
• MixEval-X: First any-to-any real-world benchmark for multi-modal evaluations
• Uses multi-modal benchmark mixture for MMU tasks
• Implements adaptation-rectification pipeline for MMG and agent tasks
• Covers 8 input-output modality combinations
• Aligns benchmark samples with real-world task distributions
• Enables periodic data refreshes to mitigate contamination
Key Insights from this Paper 💡:
• Unified evaluation standards across modalities improve AI assessment
• Real-world task distribution alignment enhances evaluation generalizability
• Dynamic benchmarking mitigates model contamination
• Efficient creation pipelines enable cost-effective, reproducible evaluations
Results 📊:
• Strong correlation with real-world user-facing evaluations (up to 0.98)
• Significantly more efficient than existing benchmarks
• Comprehensive leaderboards for reranking models and organizations
• Reveals performance gaps across different AI communities