Mathematical Formalized Problem Solving and Theorem Proving in Different Fields in Lean 4
- URL: http://arxiv.org/abs/2409.05977v2
- Date: Thu, 31 Oct 2024 16:01:59 GMT
- Title: Mathematical Formalized Problem Solving and Theorem Proving in Different Fields in Lean 4
- Authors: Xichen Tang,
- Abstract summary: Using computerized formal languages like Lean 4 to prove mathematical theorems has a significant impact on mathematical formalization.
This paper introduces the basic structure and tactics in general, determine how AI can assist the mathematical formalization process to improve its performance.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Using computerized verifiable formal languages like Lean 4 to prove mathematical theorems has a significant impact on mathematical formalization. Lean 4 offers prominent potential for advancing mathematical reasoning. However, existing efforts are limited to mathematical formalization languages in substantial online corpora and are dedicated to keeping pace with rapidly evolving languages. To bridge the gap between the traditional and computerized proof, my approach to formalizing theorem proving involves generating formal steps and complete proofs using Large Language Models (LLMs) based on Natural Language (NL) proofs. The method is to introduce the basic structure and tactics in general, determine how AI can assist the mathematical formalization process to improve its performance, and give examples of solving problems in Lean 4 comparing to NL, mainly in IMO, and a sample theorem proving in abstract algebra.
Related papers
- Alchemy: Amplifying Theorem-Proving Capability through Symbolic Mutation [71.32761934724867]
This work proposes Alchemy, a framework for data synthesis that constructs formal theorems through symbolic mutation.
For each candidate theorem in Mathlib, we identify all invocable theorems that can be used to rewrite or apply to it.
As a result, our method increases the number of theorems in Mathlib by an order of magnitude, from 110k to 6M.
arXiv Detail & Related papers (2024-10-21T08:04:21Z) - Lean-STaR: Learning to Interleave Thinking and Proving [53.923617816215774]
We present Lean-STaR, a framework for training language models to produce informal thoughts prior to each step of a proof.
Lean-STaR achieves state-of-the-art results on the miniF2F-test benchmark within the Lean theorem proving environment.
arXiv Detail & Related papers (2024-07-14T01:43:07Z) - Autoformalizing Euclidean Geometry [74.72212706513318]
We introduce a neuro-symbolic framework for autoformalizing Euclidean geometry.
One challenge is that informal proofs rely on diagrams, leaving gaps in texts that are hard to formalize.
We provide automatic semantic evaluation for autoformalized theorem statements.
arXiv Detail & Related papers (2024-05-27T14:35:10Z) - EvoGPT-f: An Evolutionary GPT Framework for Benchmarking Formal Math
Languages [0.0]
Formal mathematics is the discipline of translating mathematics into a programming language.
This paper introduces an evolutionary framework for the first systematic quantitative analysis of the differential machine learnability of five formal math corpora.
arXiv Detail & Related papers (2024-02-12T19:10:11Z) - A New Approach Towards Autoformalization [7.275550401145199]
Autoformalization is the task of translating natural language mathematics into a formal language that can be verified by a program.
Research paper mathematics requires large amounts of background and context.
We propose an avenue towards tackling autoformalization for research-level mathematics, by breaking the task into easier and more approachable subtasks.
arXiv Detail & Related papers (2023-10-12T00:50:24Z) - ProofNet: Autoformalizing and Formally Proving Undergraduate-Level
Mathematics [7.607254619341369]
We introduce ProofNet, a benchmark for autoformalization and formal proving of undergraduate-level mathematics.
The ProofNet benchmarks consists of 371 examples, each consisting of a formal theorem statement in Lean 3.
We report baseline results on statement autoformalization via in-context learning.
arXiv Detail & Related papers (2023-02-24T03:28:46Z) - Towards Autoformalization of Mathematics and Code Correctness:
Experiments with Elementary Proofs [5.045988012508899]
Autoformalization seeks to address this by translating proofs written in natural language into a formal representation that is computer-verifiable via interactive theorem provers.
We introduce a semantic parsing approach, based on the Universal Transformer architecture, that translates elementary mathematical proofs into an equivalent formalization in the language of the Coq interactive theorem prover.
arXiv Detail & Related papers (2023-01-05T17:56:00Z) - Towards a Mathematics Formalisation Assistant using Large Language
Models [5.485439959027125]
We explore the abilities of a large language model (Codex) to help with formalisation in the Lean theorem prover.
Codex is able to formalise short mathematical statements at undergrad level with nearly 75% accuracy for $120$ theorem statements.
We show that with a new prompting strategy Codex can formalise these proofs in natural language with at least one out of twelve Codex completion being easy to repair into a complete proof.
arXiv Detail & Related papers (2022-11-14T16:52:32Z) - NaturalProver: Grounded Mathematical Proof Generation with Language
Models [84.2064569475095]
Theorem proving in natural mathematical language plays a central role in mathematical advances and education.
We develop NaturalProver, a language model that generates proofs by conditioning on background references.
NaturalProver is capable of proving some theorems that require short (2-6 step) proofs, and providing next-step suggestions that are rated as correct and useful over 40% of the time.
arXiv Detail & Related papers (2022-05-25T17:01:18Z) - NaturalProofs: Mathematical Theorem Proving in Natural Language [132.99913141409968]
We develop NaturalProofs, a multi-domain corpus of mathematical statements and their proofs.
NaturalProofs unifies broad coverage, deep coverage, and low-resource mathematical sources.
We benchmark strong neural methods on mathematical reference retrieval and generation tasks.
arXiv Detail & Related papers (2021-03-24T03:14:48Z) - Generative Language Modeling for Automated Theorem Proving [94.01137612934842]
This work is motivated by the possibility that a major limitation of automated theorem provers compared to humans might be addressable via generation from language models.
We present an automated prover and proof assistant, GPT-f, for the Metamath formalization language, and analyze its performance.
arXiv Detail & Related papers (2020-09-07T19:50:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.