通过结构抽象与确定性解析，提升大模型逻辑推理的准确性与公平性。
ITLC at SemEval-2026 Task 11: Normalization and Deterministic Parsing for Formal Reasoning in LLMs
- 将三段论转化为标准逻辑形式，消除内容偏差
- 在多语言评测中所有子任务均进入前五名
- 无需微调或激活层干预，适合部署于复杂场景
大语言模型在推理任务中受内容效应影响显著，尤其在多语言环境下。本文提出一种新方法，通过显式结构抽象将三段论转换为规范逻辑表达，并采用确定性解析判断其有效性。在 SemEval-2026 Task 11 多语言基准上评估，该方法在所有子任务中均取得前五名成绩，显著降低内容偏差，且无需复杂微调或激活层干预，为提升大模型形式推理能力提供高效替代方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。