Graph Sequence Learning for Premise Selection

Edvard K. Holden; Konstantin Korovin

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arXiv · 2303.15642

Graph Sequence Learning for Premise Selection

Edvard K. Holden·Konstantin Korovin

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Abstract

Premise selection is crucial for large theory reasoning as the sheer size of the problems quickly leads to resource starvation. This paper proposes a premise selection approach inspired by the domain of image captioning, where language models automatically generate a suitable caption for a given image. Likewise, we attempt to generate the sequence of axioms required to construct the proof of a given problem. This is achieved by combining a pre-trained graph neural network with a language model. We evaluated different configurations of our method and experience a 17.7% improvement gain over the baseline.

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Edvard K. Holden, Konstantin Korovin. 2023-03-27. Graph Sequence Learning for Premise Selection. https://arxiv.org/abs/2303.15642

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