@inproceedings{luo-etal-2026-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2026 Task 11: Mitigating Content Effects in Syllogistic Reasoning with Qwen2-1.5{B}-Instruct and {XLM}-{R}o{BERT}a-Large for {E}nglish and Multilingual {T}asks{M}ultilingual Tasks",
    author = "Luo, Rongchuan  and
      Wang, Jin  and
      Zhang, Xuejie",
    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-1.40/",
    pages = "277--283",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper addresses SemEval-2026 Task 11, which focused on mitigating content effects in syllogistic reasoning. Logical validity is often conflated with semantic plausibility in large language models.Prior methods rely on standard fine-tuning or prompting, without explicit bias control.A rule- and template-based symbolic data augmentation framework is proposed for fine-tuning the {\textbackslash}texttt{\{}Qwen2-1.5B-Instruct{\}} model and instruction-tuning the {\textbackslash}texttt{\{}XLM-RoBERTa-large{\}} model. Logic-preserving synthetic data are generated through lexical rules. The system is ranked 1st in Task 1 with a perfect overall score of 100, and 6th in Task 3 with a score of 56.97. Code is publicly available at: {\textbackslash}url{\{}https://github.com/YNU-HPCC/semeval-2026-task11{\}}."
}