@inproceedings{r-chopra-2026-aicoe,
    title = "{AICOE}-Tredence at {S}em{E}val-2026 Task 11: Mitigating Content Bias in Syllogisms via Symbolic Logic-Language Decoupling",
    author = "R, Rakshith  and
      Chopra, Ankush",
    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-1.229/",
    pages = "1802--1816",
    ISBN = "979-8-89176-414-9",
    abstract = "Content bias remains a key limitation of large language models (LLMs), which often conflate formal logical validity with real-world plausibility. SemEval-2026 Task 11 examines this challenge through multilingual syllogistic reasoning, requiring models to judge validity independently of content. We propose a structure-first reasoning paradigm that abstracts natural language syllogisms into Aristotelian logical forms. By mapping arguments to mood{--}figure representations and classifying validity in this symbolic space, our approach removes semantic content from the reasoning process. On the private test sets of Subtasks 1 and 3, our method achieves a perfect combined score, with 100{\%} validity accuracy and zero content bias in both English and multilingual settings using Gemini-3 Pro Preview. We also explore transferring this paradigm to smaller models via structural supervision, finding that distilled systems retain high accuracy with minimal bias. These results suggest that explicitly separating logical form from linguistic content is a promising direction for bias-resilient and cross-lingually robust reasoning in LLMs."
}