@inproceedings{s-etal-2026-igniters,
title = "Igniters@{LTEDI} 2026: Multilingual Gender-Inclusive Language Generation with m{T}5 and Counter-Narrative Generation Using Llama-3",
author = "S, Rajendran and
N.Ramkumar and
Malarselvi",
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://aclanthology.org/2026.ltedi-1.18/",
pages = "172--176",
ISBN = "979-8-89176-424-8",
abstract = "The deployment of Large Language Models(LLMs) has intensified concerns regarding thepropagation of societal stereotypes encodedwith web-scale training corpora. This pa-per presents a dual-paradigm framework spe-cially designed to address multilingual gender-inclusvity and counterfactual generation. Formultilingual gender-neutral text transformation,a fine-tuned mT5 encoder{--}decoder model per-forms controlled sentence rewriting with mini-mal edits while preserving semantic fidelity andgrammatical fluency. For counter-narrative gen-eration, the Llama-3 8B decoder-only model isemployed to generate empathetic and persua-sive responses through structured prompt-basedgeneration. The framework is evaluated usingdatasets from the LT-EDI ACL 2026 sharedtask across multiple languages, including En-glish, Tamil, Kannada, German, and Spanish.Experimental results demonstrate strong effec-tiveness in identifying and neutralizing gendermarkers, particularly in morphologically richlanguages, while the counter-narrative compo-nent achieves high performance in politeness,coherence, and relevance. Overall, the pro-posed approach contributes toward the develop-ment of responsible and inclusive multilingualNLP systems."
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<abstract>The deployment of Large Language Models(LLMs) has intensified concerns regarding thepropagation of societal stereotypes encodedwith web-scale training corpora. This pa-per presents a dual-paradigm framework spe-cially designed to address multilingual gender-inclusvity and counterfactual generation. Formultilingual gender-neutral text transformation,a fine-tuned mT5 encoder–decoder model per-forms controlled sentence rewriting with mini-mal edits while preserving semantic fidelity andgrammatical fluency. For counter-narrative gen-eration, the Llama-3 8B decoder-only model isemployed to generate empathetic and persua-sive responses through structured prompt-basedgeneration. The framework is evaluated usingdatasets from the LT-EDI ACL 2026 sharedtask across multiple languages, including En-glish, Tamil, Kannada, German, and Spanish.Experimental results demonstrate strong effec-tiveness in identifying and neutralizing gendermarkers, particularly in morphologically richlanguages, while the counter-narrative compo-nent achieves high performance in politeness,coherence, and relevance. Overall, the pro-posed approach contributes toward the develop-ment of responsible and inclusive multilingualNLP systems.</abstract>
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%0 Conference Proceedings
%T Igniters@LTEDI 2026: Multilingual Gender-Inclusive Language Generation with mT5 and Counter-Narrative Generation Using Llama-3
%A S, Rajendran
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Thenmozhi, Durairaj
%Y García Cumbreras, Miguel Ángel
%Y Jiménez Zafra, Salud María
%A N.Ramkumar
%A Malarselvi
%S Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2026
%8 July
%I Association for Computational Linguistics
%C Virtual (Online)
%@ 979-8-89176-424-8
%F s-etal-2026-igniters
%X The deployment of Large Language Models(LLMs) has intensified concerns regarding thepropagation of societal stereotypes encodedwith web-scale training corpora. This pa-per presents a dual-paradigm framework spe-cially designed to address multilingual gender-inclusvity and counterfactual generation. Formultilingual gender-neutral text transformation,a fine-tuned mT5 encoder–decoder model per-forms controlled sentence rewriting with mini-mal edits while preserving semantic fidelity andgrammatical fluency. For counter-narrative gen-eration, the Llama-3 8B decoder-only model isemployed to generate empathetic and persua-sive responses through structured prompt-basedgeneration. The framework is evaluated usingdatasets from the LT-EDI ACL 2026 sharedtask across multiple languages, including En-glish, Tamil, Kannada, German, and Spanish.Experimental results demonstrate strong effec-tiveness in identifying and neutralizing gendermarkers, particularly in morphologically richlanguages, while the counter-narrative compo-nent achieves high performance in politeness,coherence, and relevance. Overall, the pro-posed approach contributes toward the develop-ment of responsible and inclusive multilingualNLP systems.
%U https://aclanthology.org/2026.ltedi-1.18/
%P 172-176
Markdown (Informal)
[Igniters@LTEDI 2026: Multilingual Gender-Inclusive Language Generation with mT5 and Counter-Narrative Generation Using Llama-3](https://aclanthology.org/2026.ltedi-1.18/) (S et al., LTEDI 2026)
ACL