@inproceedings{sinha-etal-2026-sigjbs-lt,
title = "{S}ig{JBS}@{LT}-{EDI} 2026: {QL}o{RA}-Tuned Homophobic and Transphobic Counter Narrative Generation",
author = "Sinha, Gaurangi and
Palacharla, Rajarajeswari and
Jagadeeshan, Manoj Balaji",
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-1.29/",
pages = "234--238",
ISBN = "979-8-89176-424-8",
abstract = "We present our approach to LT-EDI@ACL 2026 on counter-narrative generation for homophobic and transphobic comments. Generating high-quality counter-narratives in multilingual and low-resource settings remains challenging, particularly when data imbalance and script variation affect model performance. To address these issues, we explore multiple modeling strategies built around Gemma 3 12B with QLoRA fine-tuning, including data rebalancing and alternative input strategies for Tamil. Our findings show that task-specific fine-tuning combined with native-script Tamil produces more stable and higher-quality outputs than large few-shot prompts or transliteration-basedinputs. On the official leaderboard, our system ranks second in English with an overall score of 86.35{\%} and sixth in Tamil with 63.77{\%},highlighting both the effectiveness of targeted fine-tuning and the challenges of low-resource counter-narrative generation."
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<abstract>We present our approach to LT-EDI@ACL 2026 on counter-narrative generation for homophobic and transphobic comments. Generating high-quality counter-narratives in multilingual and low-resource settings remains challenging, particularly when data imbalance and script variation affect model performance. To address these issues, we explore multiple modeling strategies built around Gemma 3 12B with QLoRA fine-tuning, including data rebalancing and alternative input strategies for Tamil. Our findings show that task-specific fine-tuning combined with native-script Tamil produces more stable and higher-quality outputs than large few-shot prompts or transliteration-basedinputs. On the official leaderboard, our system ranks second in English with an overall score of 86.35% and sixth in Tamil with 63.77%,highlighting both the effectiveness of targeted fine-tuning and the challenges of low-resource counter-narrative generation.</abstract>
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%0 Conference Proceedings
%T SigJBS@LT-EDI 2026: QLoRA-Tuned Homophobic and Transphobic Counter Narrative Generation
%A Sinha, Gaurangi
%A Palacharla, Rajarajeswari
%A Jagadeeshan, Manoj Balaji
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Thenmozhi, Durairaj
%Y García Cumbreras, Miguel Ángel
%Y Jiménez Zafra, Salud María
%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 sinha-etal-2026-sigjbs-lt
%X We present our approach to LT-EDI@ACL 2026 on counter-narrative generation for homophobic and transphobic comments. Generating high-quality counter-narratives in multilingual and low-resource settings remains challenging, particularly when data imbalance and script variation affect model performance. To address these issues, we explore multiple modeling strategies built around Gemma 3 12B with QLoRA fine-tuning, including data rebalancing and alternative input strategies for Tamil. Our findings show that task-specific fine-tuning combined with native-script Tamil produces more stable and higher-quality outputs than large few-shot prompts or transliteration-basedinputs. On the official leaderboard, our system ranks second in English with an overall score of 86.35% and sixth in Tamil with 63.77%,highlighting both the effectiveness of targeted fine-tuning and the challenges of low-resource counter-narrative generation.
%U https://aclanthology.org/2026.ltedi-1.29/
%P 234-238
Markdown (Informal)
[SigJBS@LT-EDI 2026: QLoRA-Tuned Homophobic and Transphobic Counter Narrative Generation](https://aclanthology.org/2026.ltedi-1.29/) (Sinha et al., LTEDI 2026)
ACL