Large language models suffer from content effects in reasoning tasks, particularly in multi-lingual contexts. We introduce a novel method that reduces these biases through explicit structural abstraction that transforms syllogisms into canonical logical representations and applies deterministic parsing to determine validity. Evaluated on the SemEval-2026 Task 11 multilingual benchmark, our approach achieves top-5 rankings across all subtasks while substantially reducing content effects and offering a competitive alternative to complex fine-tuning or activation-level interventions.
@article{arxiv.2603.02676,
title = {ITLC at SemEval-2026 Task 11: Normalization and Deterministic Parsing for Formal Reasoning in LLMs},
author = {Wicaksono Leksono Muhamad and Joanito Agili Lopo and Tack Hwa Wong and Muhammad Ravi Shulthan Habibi and Samuel Cahyawijaya},
journal= {arXiv preprint arXiv:2603.02676},
year = {2026}
}