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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