322 citations · 426 across the 11 of their papers we have counts for
6 papers · 1 filter
Premise Selection for a Lean Hammer
Thomas Zhu, Joshua Clune, Jeremy Avigad +2
Neural methods are transforming automated reasoning for proof assistants, yet integrating these advances into practical verification workflows remains challenging. A hammer is a to…
End-to-End Ontology Learning with Large Language Models
Andy Lo, Albert Q. Jiang, Wenda Li +1
Ontologies are useful for automatic machine processing of domain knowledge as they represent it in a structured format. Yet, constructing ontologies requires substantial manual eff…
Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe
Alicja Ziarko, Albert Q. Jiang, Bartosz Piotrowski +3
Text embeddings are essential for many tasks, such as document retrieval, clustering, and semantic similarity assessment. In this paper, we study how to contrastively train text em…
Evaluating Language Models for Mathematics through Interactions
Katherine M. Collins, Albert Q. Jiang, Simon Frieder +11
There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants. However, the standard methodology of e…
Magnushammer: A Transformer-Based Approach to Premise Selection
Maciej Mikuła, Szymon Tworkowski, Szymon Antoniak +7
This paper presents a novel approach to premise selection, a crucial reasoning task in automated theorem proving. Traditionally, symbolic methods that rely on extensive domain know…
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…