@inproceedings{fernando-lopez-ponce-bel-enguix-2025-limits,
    title = "Into The Limits of Logic: Alignment Methods for Formal Logical Reasoning",
    author = "Fernando Lopez-Ponce, Francisco  and
      Bel-Enguix, Gemma",
    editor = "Valentino, Marco  and
      Ferreira, Deborah  and
      Thayaparan, Mokanarangan  and
      Ranaldi, Leonardo  and
      Freitas, Andre",
    booktitle = "Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.mathnlp-main.8/",
    pages = "112--123",
    ISBN = "979-8-89176-348-7",
    abstract = "We implement Large Language Model Alignment algorithms to formal logic reasoning tasks involving natural-language (NL) to first-order logic (FOL) translation, formal logic inference, and premise retranslation. These methodologies were implemented using task-specific preference datasets created based on the FOLIO datasets and LLM generations. Alignment was based on DPO, this algorithm was implemented and tested on off-the-shelf and pre-aligned models, showing promising results for higher quality NL-FOL parsing, as well as general alignment strategies. In addition, we introduce a new similarity metric ($LogicSim$) between LLM-generated responses and gold standard values, that measures logic-relevant information such as premise count and overlap between answers and expands evaluation of NL-FOL translation pipelines. Our results show that LLMs still struggle with logical inference, however alignment benefits semantic parsing and retranslation of results from formal logic to natural language."
}