@inproceedings{arai-etal-2025-chakoshi,
    title = "Chakoshi: A Customizable Guardrail for {LLM}s with a Focus on {J}apanese-Language Moderation",
    author = "Arai, Kazuhiro  and
      Matsui, Ryota  and
      Miyama, Kenji  and
      Yamamoto, Yudai  and
      Shibamiya, Ren  and
      Sugimoto, Kaito  and
      Iwase, Yoshimasa",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.14/",
    pages = "118--124",
    abstract = "In this research, we developed and evaluated ``chakoshi'' an LLM guardrail model designed to address Japanese-specific nuances. chakoshi is a lightweight LLM that has been fine-tuned using multiple open datasets and proprietary learning datasets. Based on gemma-2-9b, the chakoshi model achieved an average F1 score of 0.92 or higher across multiple test datasets, demonstrating superior performance compared to existing models. Additionally, we implemented a feature that allows customization of categories to be filtered using natural language, and confirmed its effectiveness through practical examples."
}