@inproceedings{adhikary-etal-2026-justgen,
    title = "{J}ust{G}en@{LT}-{EDI} 2026: Controlled Gender Inclusive and Bias-Aware Language Generation using {LLM}s",
    author = "Adhikary, Nilendu  and
      Chanda, Supriya  and
      Pal, Sukomal",
    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-1.22/",
    pages = "193--197",
    ISBN = "979-8-89176-424-8",
    abstract = "Over the past decade, the rapid advancement of LLMs has significantly improved natural language generation. However, these models often inherit and amplify gender biases present in large-scale training data, leading to stereotypical associations, androcentric language, and misgendering. Such biases can negatively impact applications in education, healthcare, legal systems, and automated content generation. In this paper, we address this issue as defined in the shared task LT-EDI on Gender-Inclusive Language Generation. The task focuses on rewriting gender-biased sentences into inclusive, gender-neutral alternatives while preserving meaning. We propose a retrieval-augmented framework combining lexical replacement, semantic retrieval, and controlled instruction-tuned generation. An edit-distance constraint and self-evaluation step ensure minimal, coherent, and bias-free outputs. We also present zero-shot adaptation for low resource language. The implementation code available here \url{https://github.com/SupriyaChanda/gilg-ltedi-acl2026.git}."
}