Natural Language to First-Order Logic LLM-based Autoformalization
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-...
| Authors | Andrea Brunello, Cristian Curaba, Luca Geatti et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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