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        <title>Few-Shot Natural Language to First-Order Logic Translation via Code Generation</title>
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            <title>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)</title>
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            <namePart type="given">Luis</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2025.naacl-long.547</identifier>
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%0 Conference Proceedings
%T Few-Shot Natural Language to First-Order Logic Translation via Code Generation
%A Liu, Junnan
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S 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)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F liu-2025-shot
%X 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.
%R 10.18653/v1/2025.naacl-long.547
%U https://aclanthology.org/2025.naacl-long.547/
%U https://doi.org/10.18653/v1/2025.naacl-long.547
%P 10939-10960

Markdown (Informal)

[Few-Shot Natural Language to First-Order Logic Translation via Code Generation](https://aclanthology.org/2025.naacl-long.547/) (Liu, NAACL 2025)

• Few-Shot Natural Language to First-Order Logic Translation via Code Generation (Liu, NAACL 2025)

ACL

• Junnan Liu. 2025. Few-Shot Natural Language to First-Order Logic Translation via Code Generation. In 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), pages 10939–10960, Albuquerque, New Mexico. Association for Computational Linguistics.