@inproceedings{yao-etal-2025-language,
    title = "Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training",
    author = "Yao, Yihang  and
      Cen, Zhepeng  and
      Li, Miao  and
      Han, William  and
      Zhang, Yuyou  and
      Liu, Emerson  and
      Liu, Zuxin  and
      Gan, Chuang  and
      Zhao, Ding",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.1182/",
    pages = "23036--23052",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks. However, even minor variations in query phrasing, despite preserving the underlying semantic meaning, can significantly affect their performance. To address this, we focus on enhancing LLMs' awareness of symmetry in query variations and propose syMmetry-ENhanceD (MEND) data augmentation, a data-centric approach that improves the model{'}s ability to extract useful information from context. Unlike existing methods that emphasize reasoning chain augmentation, our approach improves model robustness at the knowledge extraction stage through query augmentation, enabling more data-efficient training and stronger generalization to Out-of-Distribution (OOD) settings. Extensive experiments on both logical and arithmetic reasoning tasks show that MEND enhances reasoning performance across diverse query variations, providing new insights into improving LLM robustness through structured dataset curation."
}