
TurtleBench
Benchmark for LLM Reasoning & Understanding with Challenging Tasks from Real Users.
Stars: 106

TurtleBench is a dynamic evaluation benchmark that assesses the reasoning capabilities of large language models through real-world yes/no puzzles. It emphasizes logical reasoning over knowledge recall by using user-generated data from a Turtle Soup puzzle platform. The benchmark is objective and unbiased, focusing purely on reasoning abilities and providing clear, measurable outcomes for easy comparison. TurtleBench constantly evolves with real user-generated questions, making it impossible to 'game' the system. It tests the model's ability to comprehend context and make logical inferences.
README:
TurtleBench is a dynamic evaluation benchmark designed to assess the reasoning capabilities of large language models (LLMs) through real-world yes/no puzzles, emphasizing logical reasoning over knowledge recall by using user-generated data from a Turtle Soup puzzle platform.
- Objective and Unbiased: Eliminates the need for background knowledge, focusing purely on reasoning abilities.
- Quantifiable Results: Clear, measurable outcomes (correct/incorrect/unknown) for easy comparison.
- Constantly Evolving: Uses real user-generated questions, making it impossible to "game" the system.
- Language Understanding: Tests the model's ability to comprehend context and make logical inferences.
# Install dependencies
conda create -n turtle python=3.10
conda activate turtle
pip install -r requirements.txt
# Set up configuration
mv config_example.ini config.ini
# Edit config.ini to add your API key
# Run evaluations
python eval.py --shot 0 --models Claude_3_5_Sonnet --language zh --save_interval 10 --time_delay 2
# Analyze results
python analyst.py
.
├── README.md # Project description and documentation
├── analyst.py # Data analysis script
├── archived # Archived result files
├── config.ini # Configuration file (for actual run)
├── config_example.ini # Example configuration file
├── data # Directory containing project data
│ ├── en # Subdirectory for English data
│ └── zh # Subdirectory for Chinese data
├── eval.py # Evaluation script
├── insights # Analysis and visualization-related scripts
│ ├── case_handler.py # OpenAI o1 Error case handling script
│ ├── plots.ipynb # Data visualization and plot generation
│ ├── token_boxplot.py # Box plot for token usage
│ └── token_cal.py # Token calculation script
├── logs # Directory for storing logs
├── models.py # Model definition script
├── outputs # Directory for storing output results
├── prompts # Files related to prompt generation
├── requirements.txt # Project dependencies list
└── stats # Statistics and result-related files
Note: You can find detailed experiment results in the archived directory.
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