2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CL2024
Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion
Dylan Zhang, Curt Tigges, Zory Zhang +3
This paper investigates the ability of transformer-based models to learn structural recursion from examples. Recursion is a universal concept in both natural and formal languages.…
cs.FL2023★ 1 cited
Getting More out of Large Language Models for Proofs
Shizhuo Dylan Zhang, Talia Ringer, Emily First
Large language models have the potential to simplify formal theorem proving and make it more accessible. But how to get the most out of these models is still an open question. To a…
cs.LG2023★ 2 cited
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…