@inproceedings{wang-etal-2026-parallel,
    title = "Parallel Universes, Parallel Languages: A Comprehensive Study on {LLM}-based Multilingual Counterfactual Example Generation",
    author = "Wang, Qianli  and
      Nguyen, Van Bach  and
      Liu, Yihong  and
      Splitt, Fedor  and
      Feldhus, Nils  and
      Seifert, Christin  and
      Schuetze, Hinrich  and
      M{\textbackslash}{''}oller, Sebastian  and
      Schmitt, Vera",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.12/",
    pages = "323--346",
    ISBN = "979-8-89176-390-6",
    abstract = "Counterfactuals refer to minimally edited inputs that cause a model{'}s prediction to change, serving as a promising approach to explaining the model{'}s behavior. Large language models (LLMs) excel at generating English counterfactuals and demonstrate multilingual proficiency. However, their effectiveness in generating multilingual counterfactuals remains unclear. To this end, we conduct a comprehensive study on multilingual counterfactuals. We first conduct automatic evaluations on both directly generated counterfactuals in the target languages and those derived via English translation across six languages. Although translation-based counterfactuals offer higher validity than their directly generated counterparts, they demand substantially more modifications and still fall short of matching the quality of the original English counterfactuals. Second, we find the patterns of edits applied to high-resource European-language counterfactuals to be remarkably similar, suggesting that cross-lingual perturbations follow common strategic principles. Third, we reveal that multilingual counterfactual data augmentation (CDA) yields larger model performance improvements than cross-lingual CDA, especially for lower-resource languages. Yet, the imperfections of the generated counterfactuals limit gains in model performance and robustness. Finally, we identify and categorize four main types of errors that consistently appear in the generated counterfactuals across languages."
}