@inproceedings{chen-etal-2026-sylloscope,
    title = "Sylloscope at {S}em{E}val-2026 Task 11: Decoupling Logic from Belief via {D}eep{S}eek-Enhanced Distillation in Qwen Models",
    author = "Chen, Zhanyu  and
      Mu{\~n}oz Mart{\'i}n, Mar{\'i}a Teresa  and
      Huisman, Sem  and
      Lan, Jingjing",
    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-1.184/",
    pages = "1421--1428",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents our approach for SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models. We propose a neuro-symbolic teacher-student framework that utilizes DeepSeek-R1 as a Logical Auditor to generate a high-fidelity training corpus. We distill these analytical behaviors into Qwen-3 models using Low Rank Adaptation (LoRA), focusing on teaching the mechanics of logic rather than simple label matching. Our system yields robust results across both subtasks, with a ranking score of 39.81 (96.86{\%} accuracy) on Subtask 1 and 26.02 on Subtask 3. However, validity bias partially persists, so we conclude that while structured distillation substantially mitigates belief bias, fully disentangling logical validity from plausibility remains a central challenge for future development."
}