Year: 2024
Type: conference-paper
Abstract: Theorem proving presents a significant challenge for large language models (LLMs) due to the requirement for formal proofs to be rigorously checked by proof assistants, such as Lean, eliminating any margin for error or hallucination.While existing LLM-based theorem provers attempt to operate autonomously, they often struggle with novel and complex theorems where human insights are essential.Lean Copilot is a novel framework that integrates LLM inference into the Lean proof assistant environment.In this work, we benchmark performance of several LLMs including general and math-specific models for theorem proving using the Lean Copilot framework.Our initial investigation suggests that a general-purpose large model like LLaMa-70B still has edge over math-specific smaller models for the task under consideration.We provide useful insights into the performance of different LLMs we chose for the task.
Authors: Vanessa Lama, Catherine Ma, Tirthankar Ghosal
Institutions: Oak Ridge National Laboratory, Pomona College
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404781441