118 citations · 344 across the 22 of their papers we have counts for
23 papers
Baldur: Whole-Proof Generation and Repair with Large Language Models
Emily First, Markus N. Rabe, Talia Ringer +1
Formally verifying software properties is a highly desirable but labor-intensive task. Recent work has developed methods to automate formal verification using proof assistants, suc…
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…
Self-attention Does Not Need Memory
Markus N. Rabe, Charles Staats
We present a very simple algorithm for attention that requires memory with respect to sequence length and an extension to self-attention that requires memory. Th…
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…
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
Yuhuai Wu, Markus Rabe, Wenda Li +3
While designing inductive bias in neural architectures has been widely studied, we hypothesize that transformer networks are flexible enough to learn inductive bias from suitable g…