43 citations · 117 across the 7 of their papers we have counts for
7 papers · 1 filter
Autoformalization with Large Language Models
Yuhuai Wu, Albert Q. Jiang, Wenda Li +4
Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs. A successful autoformalization system could adv…
Memorizing Transformers
Yuhuai Wu, Markus N. Rabe, DeLesley Hutchins +1
Language models typically need to be trained or finetuned in order to acquire new knowledge, which involves updating their weights. We instead envision language models that can sim…
Neural Circuit Synthesis from Specification Patterns
Frederik Schmitt, Christopher Hahn, Markus N. Rabe +1
We train hierarchical Transformers on the task of synthesizing hardware circuits directly out of high-level logical specifications in linear-time temporal logic (LTL). The LTL synt…
Mathematical Reasoning via Self-supervised Skip-tree Training
Markus N. Rabe, Dennis Lee, Kshitij Bansal +1
We examine whether self-supervised language modeling applied to mathematical formulas enables logical reasoning. We suggest several logical reasoning tasks that can be used to eval…
Mathematical Reasoning in Latent Space
Dennis Lee, Christian Szegedy, Markus N. Rabe +2
We design and conduct a simple experiment to study whether neural networks can perform several steps of approximate reasoning in a fixed dimensional latent space. The set of rewrit…
Learning to Reason in Large Theories without Imitation
Kshitij Bansal, Christian Szegedy, Markus N. Rabe +2
In this paper, we demonstrate how to do automated theorem proving in the presence of a large knowledge base of potential premises without learning from human proofs. We suggest an…