Work

MiniF2F in Rocq: Automatic Translation Between Proof Assistants -- A Case Study

Year: 2025

Type: preprint

Abstract: In this work, we conduct an experiment using state-of-the-art LLMs to translate MiniF2F into Rocq. The translation task focuses on generating a Rocq theorem based on three sources: a natural language description, the Lean formalization, and the Isabelle formalization. We conducted our experiment in 3 stages of increasing complexity, from basic one-shot prompting to multi-turn conversations that incorporate feedback from unsuccessful attempts. At each stage, we perform multiple rounds of translation using increasingly advanced models: GPT-4o mini, Claude 3.5 Sonnet, o1 mini, and o1. We successfully translated 478 out of 488 theorems. The dataset is opensource: https://github.com/LLM4Rocq/miniF2F-rocq.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2503.04763

Authors: Viennot, Jules, Baudart, Guillaume, Arias, Emilio Jesùs Gallego, Lelarge, Marc

Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Université Paris Cité, Sorbonne Paris Cité, Institut de Recherche en Informatique Fondamentale

Language: English

Topic: Logic, programming, and type systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, HORIZON EUROPE Framework Programme

OpenAlex ID: W6966938476

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