@inproceedings{biesterbos-etal-2026-rvh,
    title = "{R}v{H}-40 at {S}em{E}val-2026 Task 11: Disentangling Reasoning from Belief through Symbolic Abstraction",
    author = "Biesterbos, Niek  and
      Den Ouden, Mark  and
      De Rijke, Janiek",
    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-1.65/",
    pages = "451--456",
    ISBN = "979-8-89176-414-9",
    abstract = "Large Language Models (LLMs) often struggle with syllogistic reasoning due to ``belief bias,'' where semantic world knowledge overrides formal logical structure. In this paper, we present our submission for the SemEval-2026 Task 11 shared task. We investigate the discrepancy between a model{'}s latent logical capabilities and its performance on natural language text. By employing symbolic transformations, specifically variable and pseudoword substitution, we demonstrate that models like Qwen2.5-14B possess strong inherent reasoning skills that are suppressed by linguistic content. We propose a ``logic alignment'' strategy using Low-Rank Adaptation (LoRA) to bridge this gap. Our final model achieved a near-perfect accuracy of 97.92{\%} on the validation set and 96.34{\%} on the official hidden test set, effectively eliminating content bias while maintaining robust generalization across abstract formats."
}