University of Illinois Urbana-Champaign

Neural approaches to theorem search & proof repair

Reichel, Thomas

Content Files
REICHEL-THESIS-2024.pdf

Permalink

https://hdl.handle.net/2142/125634

Description

Title
Neural approaches to theorem search & proof repair
Author(s)
Reichel, Thomas
Issue Date
2024-07-16
Director of Research (if dissertation) or Advisor (if thesis)
Ringer, Talia
Department of Study
Siebel Computing &DataScience
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • machine learning
  • formal methods
  • large language models
  • proof repair
  • theorem search
  • natural language search
  • Coq
Abstract
This interdisciplinary formal methods/machine learning thesis builds neural automation for two proof-centric tasks that catalyze the reuse of existing proofs: (1) natural language theorem search, in which theorems and their corresponding proofs are retrieved from a database using natural language descriptions and (2) proof repair, in which proofs broken by external changes are mended. The theorem search model is also used as a component of the proof repair tool, allowing it to better interact with the environment. Each task is tackled holistically: we contribute datasets, fine-tuned large language models, and the end-user tools needed to make use of those models.
Graduation Semester
2024-08
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/125634
Copyright and License Information
Copyright 2024 Thomas Reichel

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois