An Evaluation Benchmark for Autoformalization in Lean4
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Abstract (arXiv)
Large Language Models (LLMs) hold the potential to revolutionize autoformalization. The introduction of Lean4, a mathematical programming language, presents an unprecedented opportunity to rigorously assess the autoformalization capabilities of LLMs. This paper introduces a novel evaluation benchmark designed for Lean4, applying it to test the abilities of state-of-the-art LLMs, including GPT-3.5, GPT-4, and Gemini Pro. Our comprehensive analysis reveals that, despite recent advancements, these LLMs still exhibit limitations in autoformalization, particularly in more complex areas of mathematics. These findings underscore the need for further development in LLMs to fully harness their potential in scientific research and development. This study not only benchmarks current LLM capabilities but also sets the stage for future enhancements in autoformalization.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 0 of its 4 references that are in the map. First posted on arXiv 1 Jun 2024.
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Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025Automated Formalization via Conceptual Retrieval-Augmented LLMscited by 5
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- 2026Autoformalizing Memory Specifications with Agents30 Apr 2026
- 2025Improving Autoformalization Using Direct Dependency Retrieval15 Nov 2025
- 2025Automated Formalization via Conceptual Retrieval-Augmented LLMs9 Aug 2025