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LEAN-GitHub: A Large-Scale Dataset for Advancing Automated Theorem Proving

LEAN-GitHub: A Large-Scale Dataset for Advancing Automated Theorem Proving

Practical Solutions and Value in AI for Theorem Proving

Challenges in Theorem Proving

Theorem proving in mathematics faces increasing complexity, requiring substantial human effort to create computer-verifiable proofs. Data scarcity and the complexity of formal languages limit the performance of large language models (LLMs) in solving math problems.

Evolution of Theorem Proving

Modern proof assistants and the integration of large language models have advanced the field, utilizing deep transformer-based methods and data extraction tools. Researchers have proposed innovative solutions to expand valuable data for training theorem-proving models.

LEAN-GitHub Dataset Construction

Researchers from The Chinese University of Hong Kong have developed LEAN-GitHub, a large-scale Lean dataset on GitHub, addressing compilation challenges and enhancing extraction efficiency. The resulting dataset is diverse, covering various mathematical fields and offering a unique combination of human-written content and intermediate states.

Impact of LEAN-GitHub Dataset

Models trained on the LEAN-GitHub dataset demonstrate exceptional formal reasoning abilities, achieving state-of-the-art performance and improving reasoning capabilities across various mathematical fields and difficulty levels.

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Vladimir Dyachkov, Ph.D
Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

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