Turtle Benchmark

Evaluating the logical reasoning and context comprehension abilities of large language models.

CommonProductProgrammingBenchmarkingLogical Reasoning

Turtle Benchmark is a new, cheat-proof benchmark based on the 'Turtle Soup' game, focusing on the assessment of large language models (LLMs) in terms of logical reasoning and context comprehension. By eliminating the need for background knowledge, it provides objective and unbiased test results with quantifiable outcomes, ensuring that models cannot be 'gamed' through the use of real user-generated questions.

Clear and unbiased objectives: Focuses on reasoning capabilities without requiring background knowledge.
Quantifiable results: Offers clear
measurable outcomes (correct/incorrect/unknown) for easy comparison.
Continual evolution: Utilizes real user-generated questions to prevent system manipulation.
Language understanding: Tests the model’s ability to comprehend context and perform logical inference.
Ease of use: Evaluations can be conducted through simple command line operations.
Rich data: Comprised of 32 unique 'Turtle Soup' stories and 1537 manually labeled annotations.
Results interpretation: Compare different models' overall accuracy and average story accuracy in 2-shot learning scenarios using scatter plots.

\Turtle Benchmark is designed for researchers and developers who need to evaluate and compare the performance of large language models. It is particularly suited for professionals focused on the model's logical reasoning and context comprehension capabilities
helping them gain a more accurate understanding of the model's performance in Chinese contexts.\

Researchers use Turtle Benchmark to assess the performance of different large language models on specific logical reasoning tasks.
Developers utilize Turtle Benchmark to test if their language models can accurately understand and respond to user queries.
Educational institutions use Turtle Benchmark as a teaching tool to help students understand how large language models operate and methods for performance evaluation.

1. Navigate to the Turtle Benchmark project directory.
2. Rename the .env.example file to .env and add your API key.
3. Execute the command `python evaluate.py` to conduct a 2-shot learning evaluation.
4. For zero-shot evaluation
run the command `python evaluate.py --shot 0`.

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