RealBench: Real-world Benchmark for Generative AI

🎯 Mission

RealBench addresses the critical gap in AI evaluation by testing models on practical, real-world tasks that humans actually use AI for, emphasizing consistency, practical utility, and robust handling of edge cases.

🔍 Problem Statement

Current AI benchmarks fail to capture real-world usage patterns. Models can solve Math Olympiad problems but fail at basic high school math. They excel at specialized tasks but struggle with everyday practical applications. RealBench bridges this gap.

📊 Benchmark Categories

1. RealBench-Professional

Workplace and business-oriented tasks

• Email composition with context awareness
• Report analysis and summarization
• Meeting notes to action items
• Code review and documentation
• Project planning and estimation
• Customer support responses
• Technical troubleshooting

2. RealBench-Daily

Everyday life and personal tasks

• Recipe adaptation with dietary restrictions
• Travel planning with constraints
• Personal finance advice
• Home improvement guidance
• Health and wellness questions
• Shopping comparisons
• Schedule optimization

3. RealBench-Creative

Content generation and artistic tasks

• Story continuation with consistency
• Marketing copy variations
• Social media content adaptation
• Educational content creation
• Creative writing prompts
• Image description generation
• Brand voice matching

4. RealBench-Technical

Engineering and scientific tasks

• Debug code with incomplete context
• System design from requirements
• Data analysis interpretation
• Algorithm optimization
• Security vulnerability assessment
• Performance troubleshooting
• API documentation generation

5. RealBench-Academic

Educational and research tasks

• Homework help with learning focus
• Research paper summarization
• Concept explanation at different levels
• Study guide creation
• Citation formatting
• Literature review assistance
• Exam preparation strategies

6. RealBench-Safety

Safety-critical and edge cases

• Harmful request rejection
• Misinformation detection
• Bias recognition
• Privacy-preserving responses
• Emergency situation guidance
• Medical disclaimer awareness
• Legal limitation acknowledgment

🎪 Key Features

Consistency Testing

• Same concept tested across multiple difficulty levels
• Cross-domain knowledge integration
• Multi-turn conversation coherence

Practical Metrics

• Task completion rate
• Consistency score
• Uncertainty calibration
• Hallucination detection
• Response appropriateness

Real-world Alignment

• Based on actual user queries
• Includes ambiguous scenarios
• Tests for "I don't know" responses
• Measures practical helpfulness

📁 Project Structure

RealBench/
├── README.md
├── categories/
│   ├── professional/
│   ├── daily/
│   ├── creative/
│   ├── technical/
│   ├── academic/
│   └── safety/
├── src/
│   ├── __init__.py
│   ├── benchmark_runner.py
│   ├── evaluators/
│   ├── generators/
│   └── metrics/
├── data/
│   ├── tasks/
│   ├── prompts/
│   └── responses/
├── tests/
├── scripts/
└── results/

🚀 Getting Started

Installation

pip install realbench

Quick Start

from realbench import RealBenchmark

# Initialize benchmark
benchmark = RealBenchmark()

# Run specific category
results = benchmark.run(
    model="gpt-4",
    categories=["professional", "daily"]
)

# View detailed metrics
benchmark.analyze(results)

📈 Evaluation Metrics

1. Accuracy: Correctness of responses
2. Consistency: Stability across similar queries
3. Completeness: Task completion rate
4. Appropriateness: Context-aware responses
5. Safety: Harmful content avoidance
6. Calibration: Uncertainty expression
7. Efficiency: Token usage optimization

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

📄 License

MIT License

🌟 Citation

@misc{realbench2024,
  title={RealBench: A Practical Real-world Benchmark for Generative AI},
  author={RealBench Team},
  year={2024},
  url={https://github.com/username/RealBench}
}