@inproceedings{petersen-etal-2026-hhu,
    title = "{HHU}-{S}y{L}o at {S}em{E}val-2026 Task 11: Logic in the Loop {--} Hybridizing {LLM}s and Theorem Provers for Robust Formal Reasoning",
    author = "Petersen, Wiebke  and
      Jaziri, Cherine  and
      Tran, Diem",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.400/",
    pages = "3188--3198",
    ISBN = "979-8-89176-414-9",
    abstract = "We present our system for SemEval-2026 Task 11 on reasoning disentanglement, separating syllogistic validity from semantic plausibility. We compare direct neural inference against two neuro-symbolic pipelines: translation to first-order logic and to syllogistic triples. By offloading inference to symbolic theorem provers, these hybrid models effectively mitigate content bias and improve logical fidelity."
}