@inproceedings{gajawada-etal-2026-neuni,
title = "{NEUNI}@{LT}-{EDI} 2026: Counter Narrative Generation on Homophobic and Transphobic Comments",
author = "Gajawada, Preethi and
Yanamadala, Bhanu Harsha and
Kar, Akankshya and
Wadhwa, Sahil and
Chaudhary, Divya",
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-1.24/",
pages = "206--211",
ISBN = "979-8-89176-424-8",
abstract = "Counter Narrative (CN) generation via Large Language Models (LLMs) offers a scalable approach to combating hate speech by producing targeted responses that challenge harmful content. However, existing methods typically require costly post-training or fine-tuning to improve narrative diversity and quality. We introduce a fine-tuning-free prompt optimization technique that enhances Counter Narrative effectiveness without additional model training, making it both resource-efficient and readily deployable. We conduct extensive evaluation on hate speech datasets spanning English and Tamil, employing both reference-based metrics and rubric-based LLM-as-a-judge scoring to capture multiple dimensions of narrative quality. Experiments across multiple LLMs demonstrate that our approach consistently outperforms vanilla prompting baselines, exhibits strong transferability across models, and adapts seamlessly to new evaluation metrics{---}requiring no architectural or procedural changes. Our findings suggest that carefully optimized prompting strategies can match or exceed the performance of more resource-intensive approaches, offering a practical path toward scalable hate speech intervention."
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<abstract>Counter Narrative (CN) generation via Large Language Models (LLMs) offers a scalable approach to combating hate speech by producing targeted responses that challenge harmful content. However, existing methods typically require costly post-training or fine-tuning to improve narrative diversity and quality. We introduce a fine-tuning-free prompt optimization technique that enhances Counter Narrative effectiveness without additional model training, making it both resource-efficient and readily deployable. We conduct extensive evaluation on hate speech datasets spanning English and Tamil, employing both reference-based metrics and rubric-based LLM-as-a-judge scoring to capture multiple dimensions of narrative quality. Experiments across multiple LLMs demonstrate that our approach consistently outperforms vanilla prompting baselines, exhibits strong transferability across models, and adapts seamlessly to new evaluation metrics—requiring no architectural or procedural changes. Our findings suggest that carefully optimized prompting strategies can match or exceed the performance of more resource-intensive approaches, offering a practical path toward scalable hate speech intervention.</abstract>
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%0 Conference Proceedings
%T NEUNI@LT-EDI 2026: Counter Narrative Generation on Homophobic and Transphobic Comments
%A Gajawada, Preethi
%A Yanamadala, Bhanu Harsha
%A Kar, Akankshya
%A Wadhwa, Sahil
%A Chaudhary, Divya
%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 gajawada-etal-2026-neuni
%X Counter Narrative (CN) generation via Large Language Models (LLMs) offers a scalable approach to combating hate speech by producing targeted responses that challenge harmful content. However, existing methods typically require costly post-training or fine-tuning to improve narrative diversity and quality. We introduce a fine-tuning-free prompt optimization technique that enhances Counter Narrative effectiveness without additional model training, making it both resource-efficient and readily deployable. We conduct extensive evaluation on hate speech datasets spanning English and Tamil, employing both reference-based metrics and rubric-based LLM-as-a-judge scoring to capture multiple dimensions of narrative quality. Experiments across multiple LLMs demonstrate that our approach consistently outperforms vanilla prompting baselines, exhibits strong transferability across models, and adapts seamlessly to new evaluation metrics—requiring no architectural or procedural changes. Our findings suggest that carefully optimized prompting strategies can match or exceed the performance of more resource-intensive approaches, offering a practical path toward scalable hate speech intervention.
%U https://aclanthology.org/2026.ltedi-1.24/
%P 206-211
Markdown (Informal)
[NEUNI@LT-EDI 2026: Counter Narrative Generation on Homophobic and Transphobic Comments](https://aclanthology.org/2026.ltedi-1.24/) (Gajawada et al., LTEDI 2026)
ACL