@inproceedings{liu-2025-shot,
    title = "Few-Shot Natural Language to First-Order Logic Translation via Code Generation",
    author = "Liu, Junnan",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.naacl-long.547/",
    pages = "10939--10960",
    ISBN = "979-8-89176-189-6",
    abstract = "Translation of natural language to first-order logical formula (NL-FOL) has recently gained significant attention for its critical role in logic-based NLP applications. Some studies attempt to utilize pretrained language models in a sequence-to-sequence manner for the NL-FOL task. However, these methods encounter challenges such as (1) inconsistency between the training and inference phases and (2) the data-intensive and resource-intensive finetuning process. This paper introduces a novel NL-FOL translation method, dubbed Code4Logic, which is based on in-context learning and employs code snippets to bridge the gap between natural language and first-order logic. By converting the translation task into a progressive code generation task, Code4Logic demonstrates strong generalization within a training-free manner, and enhances the performance of large language models (LLMs) to generate complex first-order logical formulas. Experimental results on NL-FOL task and downstream task datasets indicate that Code4Logic surpasses prominent training-free baselines and is comparable to supervised models trained on the full training data."
}