Hierarchical Attention Generates Better Proofs

• URL: http://arxiv.org/abs/2504.19188v1
• Date: Sun, 27 Apr 2025 10:35:05 GMT
• Title: Hierarchical Attention Generates Better Proofs
• Authors: Jianlong Chen, Chao Li, Yang Yuan, Andrew C Yao,
• Abstract summary: We introduce textbfHierarchical Attention, a regularization method that aligns attention mechanisms with mathematical reasoning structures.<n>Our approach establishes a five-level hierarchy from foundational elements to high-level concepts, ensuring structured information flow in proof generation.
• Score: 8.676187819105298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have shown promise in formal theorem proving, but their token-level processing often fails to capture the inherent hierarchical nature of mathematical proofs. We introduce \textbf{Hierarchical Attention}, a regularization method that aligns LLMs' attention mechanisms with mathematical reasoning structures. Our approach establishes a five-level hierarchy from foundational elements to high-level concepts, ensuring structured information flow in proof generation. Experiments demonstrate that our method improves proof success rates by 2.05\% on miniF2F and 1.69\% on ProofNet while reducing proof complexity by 23.81\% and 16.50\% respectively. The code is available at https://github.com/Car-pe/HAGBP.

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