Natural Language to First-Order Logic LLM-based Autoformalization

Abstract:Large Language Models (LLMs) have renewed interest in autoformalization. Yet, when First-Order Logic (FOL) is considered as the target formalism, the field still lacks a unified task formulation and a systematic survey. This paper addresses this gap: we first provide a principled definition for the FOL-autoformalization task by distinguishing Ontology Extraction from Logical Translation, showing how their conflation obscures (cross-study) evaluation; we review existing datasets, evaluation metrics, and LLM-based methods, including fine-tuning, prompting, and verification-based refinement; we identify open challenges in benchmarking, semantic evaluation, ontology-aware methods, and end-to-end applications.

Computation and Language (cs.CL)

arXiv:2610.12030 [cs.CL]

(or arXiv:2610.12030v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.12030 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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