@inproceedings{krasnodebska-etal-2026-safety,
    title = "Safety of Large Language Models Beyond {E}nglish: A Systematic Literature Review of Risks, Biases, and Safeguards",
    author = "Krasnod{\k{e}}bska, Aleksandra  and
      Dziewulska, Katarzyna  and
      Seweryn, Karolina  and
      Chrabaszcz, Maciej  and
      Kusa, Wojciech",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.44/",
    pages = "1003--1034",
    ISBN = "979-8-89176-380-7",
    abstract = "As Large Language Models (LLMs) continue to evolve, ensuring their safety across multiple languages has become a critical concern. While LLMs demonstrate impressive capabilities in English, their safety mechanisms may not generalize effectively to other languages, leading to disparities in toxicity detection, bias mitigation, and harm prevention. This systematic review examines the multilingual safety of LLMs by synthesizing findings from recent studies that evaluate their robustness across diverse linguistic and cultural contexts beyond English language. Our review explores the methodologies used to assess multilingual safety, identifies challenges such as dataset availability and evaluation biases. Based on our analysis we highlight gaps in multilingual safety research and provide recommendations for future work. This review aims to contribute to the development of fair and effective safety mechanisms for LLMs across all languages. We provide the extracted data in an interactive Streamlit dashboard, enabling transparent access to the raw data and allowing for continuous updates."
}