Neural approaches to theorem search & proof repair
Reichel, Thomas
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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.
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