atfortes/LLMSymbolicReasoningBench

Synthetic data generation for evaluating LLM symbolic and logic reasoning

28
/ 100
Experimental

This project helps AI researchers and machine learning engineers create specialized training and evaluation data for large language models. It takes descriptions of symbolic reasoning tasks (like logic puzzles or specific linguistic patterns) and generates synthetic datasets. The output is custom-tailored data that helps evaluate how well an LLM handles complex reasoning challenges.

Use this if you need to generate unique, synthetic datasets to rigorously test and improve the symbolic and logic reasoning capabilities of your large language models.

Not ideal if you're looking for pre-existing, public datasets, or if your primary focus is on fine-tuning LLMs for tasks that don't heavily involve symbolic or logical reasoning.

LLM evaluation synthetic data generation AI research natural language processing reasoning benchmarks
No License No Package No Dependents
Maintenance 10 / 25
Adoption 6 / 25
Maturity 8 / 25
Community 4 / 25

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22

Forks

1

Language

Python

License

Last pushed

Mar 06, 2026

Commits (30d)

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