A New Approach Towards Autoformalization
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Abstract (arXiv)
Verifying mathematical proofs is difficult, but can be automated with the assistance of a computer. Autoformalization is the task of automatically translating natural language mathematics into a formal language that can be verified by a program. This is a challenging task, and especially for higher-level mathematics found in research papers. Research paper mathematics requires large amounts of background and context. In this paper, we propose an avenue towards tackling autoformalization for research-level mathematics, by breaking the task into easier and more approachable subtasks: unlinked formalization (formalization with unlinked definitions and theorems), entity linking (linking to the proper theorems and definitions), and finally adjusting types so it passes the type checker. In addition, we present arXiv2Formal, a benchmark dataset for unlinked formalization consisting of 50 theorems formalized for the Lean theorem prover sampled from papers on arXiv.org. We welcome any contributions from the community to future versions of this dataset.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 12 Oct 2023.
Builds on
10 of 24 references.
References · 10
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
- 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Modelscited by 427
- 2021MiniF2F: a cross-system benchmark for formal Olympiad-level mathematicscited by 391
- 2022Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofscited by 291
- 2022Autoformalization with Large Language Modelscited by 263
- 2019The Lean mathematical librarycited by 134
- 2021Proof Artifact Co-training for Theorem Proving with Language Modelscited by 132
- 2022Towards a Mathematics Formalisation Assistant using Large Language Modelscited by 22
Built on by
Citing papers per year: 2024: 2; 2025: 5; 2026: 3.
10 papers.
- 2024Herald: A Natural Language Annotated Lean 4 Datasetcited by 59
- 2024A Survey on Deep Learning for Theorem Provingcited by 57
- 2025Autoformalization in the Era of Large Language Models: A Surveycited by 21
- 2025Aria: An Agent For Retrieval and Iterative Auto-Formalization via Dependency Graphcited by 21
- 2025ProofFlow: A Dependency Graph Approach to Faithful Proof Autoformalizationcited by 21
- 2026LemmaBench: A Live, Research-Level Benchmark to Evaluate LLM Capabilities in Mathematicscited by 10
- 2025FormaRL: Enhancing Autoformalization with no Labeled Datacited by 9
- 2026Theory-Level Autoformalization: From Isolated Statements to Unified Formal Knowledge Basescited by 3
- 2025KELPS: A Framework for Verified Multi-Language Autoformalization via Semantic-Syntactic Alignmentcited by 1
- 2026TCSAlgBench: Benchmarking Automated Proving for Research-Level Theoretical Computer Sciencecited by 0
- 2026TCSAlgBench: Benchmarking Automated Proving for Research-Level Theoretical Computer Science28 Sep 2026
- 2026Theory-Level Autoformalization: From Isolated Statements to Unified Formal Knowledge Bases14 Jul 2026
- 2026LemmaBench: A Live, Research-Level Benchmark to Evaluate LLM Capabilities in Mathematics27 Feb 2026
- 2025ProofFlow: A Dependency Graph Approach to Faithful Proof Autoformalization13 Oct 2025
- 2025Aria: An Agent For Retrieval and Iterative Auto-Formalization via Dependency Graph6 Oct 2025
- 2025FormaRL: Enhancing Autoformalization with no Labeled Data26 Aug 2025
- 2025KELPS: A Framework for Verified Multi-Language Autoformalization via Semantic-Syntactic Alignment11 Jul 2025
- 2025Autoformalization in the Era of Large Language Models: A Survey29 May 2025
- 2024Herald: A Natural Language Annotated Lean 4 Dataset9 Oct 2024
- 2024A Survey on Deep Learning for Theorem Proving15 Apr 2024