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.Comment: 17 page