@inproceedings{nair-etal-2026-cai,
    title = "{CAI}@{LTEDI} 2026: Multilingual Gender Inclusive Language Generation using Instruction-Guided m{T}5 Transformer Model",
    author = "Nair, Aiswariya p  and
      Bhagya, Sree S  and
      Jacob, Chinnu",
    editor = "Chakravarthi, Bharathi Raja  and
      B, Bharathi  and
      Buitelaar, Paul  and
      Thenmozhi, Durairaj  and
      Garc{\'i}a Cumbreras, Miguel {\'A}ngel  and
      Jim{\'e}nez Zafra, Salud Mar{\'i}a",
    booktitle = "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = jul,
    year = "2026",
    address = "Virtual (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.ltedi-1.14/",
    pages = "150--154",
    ISBN = "979-8-89176-424-8",
    abstract = "Gender bias in multilingual language generation systems poses serious ethical and social issues, especially in languages with complex morphology. In this study, we propose a lightweight multilingual approach that employs instruction-guided fine-tuning of the mT5-small transformer model for gender-inclusive language generation. The framework accommodates five languages: English, German, Spanish, Tamil, and Kannada. The approach uses the task-prefix rewriting method to transform gender-specific sentences to their gender-neutral versions. The training data from different languages is combined into a single multi-lingual dataset for sequence-to-sequence fine-tuning. Beam search decoding with repetition constraints is used during inference to improve the quality of the output. The system{'}s performance is measured using GIFI, semantic similarity, and an overall combined score across all languages. Experimental results show that the system can eliminate gender-biased language while retaining semantic meaning in part across languages"
}