@inproceedings{ellinger-etal-2026-controlling,
    title = "Controlling Cross-Lingual Answer Distributions in Language Models: Enabling Transfer of Factual Preferences",
    author = "Ellinger, Lukas  and
      Manev, Alexander  and
      Groh, Georg",
    editor = "Ma, Weicheng  and
      Vosoughi, Soroush  and
      Gillani, Nabeel  and
      Coto-Solano, Rolando",
    booktitle = "Proceedings of the 1st Workshop on Stereotypes Across Cultures in Language Technologies ({S}tere{AC}u{LT} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.stereacult-1.4/",
    pages = "35--49",
    ISBN = "979-8-89176-408-8",
    abstract = "Multilingual large language models exhibit systematic differences in their outputs across languages, even when representing the same underlying knowledge. Prior work has primarily focused on evaluating or reducing such inconsistencies. In this work, we instead study whether cross-lingual behavior can be controlled: specifically, whether answer distributions associated with other languages can be expressed under English prompting. To this end, we construct a human-annotated factual dataset and a cultural scenarios dataset, and compare intervention methods including persona prompting, activation steering, and preference-based fine-tuning. We evaluate how these methods affect answer distributions and their generalization to culturally grounded settings. Our results show that answer distributions can be systematically shifted toward those observed in other languages, with persona prompting consistently outperforming more complex intervention methods."
}