@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://aclanthology.org/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{\}}."
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<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 \textbackslashtexttt{Qwen2-1.5B-Instruct} model and instruction-tuning the \textbackslashtexttt{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: \textbackslashurl{https://github.com/YNU-HPCC/semeval-2026-task11}.</abstract>
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%0 Conference Proceedings
%T YNU-HPCC at SemEval-2026 Task 11: Mitigating Content Effects in Syllogistic Reasoning with Qwen2-1.5B-Instruct and XLM-RoBERTa-Large for English and Multilingual TasksMultilingual Tasks
%A Luo, Rongchuan
%A Wang, Jin
%A Zhang, Xuejie
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F luo-etal-2026-ynu
%X 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 \textbackslashtexttt{Qwen2-1.5B-Instruct} model and instruction-tuning the \textbackslashtexttt{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: \textbackslashurl{https://github.com/YNU-HPCC/semeval-2026-task11}.
%U https://aclanthology.org/2026.semeval-1.40/
%P 277-283Markdown (Informal)
[YNU-HPCC at SemEval-2026 Task 11: Mitigating Content Effects in Syllogistic Reasoning with Qwen2-1.5B-Instruct and XLM-RoBERTa-Large for English and Multilingual TasksMultilingual Tasks](https://aclanthology.org/2026.semeval-1.40/) (Luo et al., SemEval 2026)
ACL