Strong-AI-Lab/Logical-and-abstract-reasoning

Evaluation on Logical Reasoning and Abstract Reasoning Challenges

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Emerging

This tool helps AI researchers and practitioners evaluate and fine-tune Large Language Models (LLMs) specifically on logical and abstract reasoning challenges. It takes existing LLM configurations and various reasoning datasets as input, then outputs performance metrics in a CSV file, showing how well the models understand and apply logic. Users can also fine-tune HuggingFace models on specific reasoning datasets to improve their performance.

No commits in the last 6 months.

Use this if you are an AI researcher or machine learning engineer looking to rigorously test and improve the logical and abstract reasoning capabilities of Large Language Models.

Not ideal if you are a general user looking for a pre-trained LLM for day-to-day tasks or if you are not comfortable with command-line interfaces and model configuration files.

AI research Large Language Models model evaluation natural language processing reasoning benchmarks
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 16 / 25

How are scores calculated?

Stars

29

Forks

6

Language

Python

License

MIT

Last pushed

Apr 21, 2025

Commits (30d)

0

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