@inproceedings{pranesh-etal-2026-justicebots,
title = "{J}ustice{B}ots@{LT}-{EDI} 2026: Prompt-Based Counter-Narrative Generation for Homophobia and Transphobia Comments",
author = "Pranesh, TT and
K.K.Thamizhmathi and
Vigneshwaran, S and
B, Bharathi",
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-1.21/",
pages = "188--192",
ISBN = "979-8-89176-424-8",
abstract = "Online platforms increasingly host hate speechtargeting marginalized communities, includ-ing homophobic and transphobic commentsdirected at LGBTQ+ individuals. Counter-narratives provide a constructive way to re-spond to harmful speech by promoting em-pathy, factual clarification, and respectful di-alogue.In this work, we participate in the Shared Taskon Counter-Narrative Generation on Homopho-bic and Transphobic Comments at LT-EDI @ACL 2026. We adopt a zero-shot promptingapproach using large language models accessedthrough publicly available AI tools, includingGPT-4o, Gemini 1.5 Pro, and Llama-3 SonarLarge via Perplexity AI. Instead of traininga task-specific model, we design a structuredprompt that guides the models to generate re-spectful, concise, and contextually appropriatecounter-narratives.Experiments were conducted on English andTamil comments provided by the organiz-ers. Results demonstrate that prompt-basedgeneration can produce meaningful multilin-gual counter-narratives without additional train-ing. Our approach highlights the potential oflarge language models as lightweight tools forcounter-speech generation in multilingual on-line environments."
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<abstract>Online platforms increasingly host hate speechtargeting marginalized communities, includ-ing homophobic and transphobic commentsdirected at LGBTQ+ individuals. Counter-narratives provide a constructive way to re-spond to harmful speech by promoting em-pathy, factual clarification, and respectful di-alogue.In this work, we participate in the Shared Taskon Counter-Narrative Generation on Homopho-bic and Transphobic Comments at LT-EDI @ACL 2026. We adopt a zero-shot promptingapproach using large language models accessedthrough publicly available AI tools, includingGPT-4o, Gemini 1.5 Pro, and Llama-3 SonarLarge via Perplexity AI. Instead of traininga task-specific model, we design a structuredprompt that guides the models to generate re-spectful, concise, and contextually appropriatecounter-narratives.Experiments were conducted on English andTamil comments provided by the organiz-ers. Results demonstrate that prompt-basedgeneration can produce meaningful multilin-gual counter-narratives without additional train-ing. Our approach highlights the potential oflarge language models as lightweight tools forcounter-speech generation in multilingual on-line environments.</abstract>
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%0 Conference Proceedings
%T JusticeBots@LT-EDI 2026: Prompt-Based Counter-Narrative Generation for Homophobia and Transphobia Comments
%A Pranesh, T. T.
%A Vigneshwaran, S.
%A B, Bharathi
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Thenmozhi, Durairaj
%Y García Cumbreras, Miguel Ángel
%Y Jiménez Zafra, Salud María
%A K.K.Thamizhmathi
%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 pranesh-etal-2026-justicebots
%X Online platforms increasingly host hate speechtargeting marginalized communities, includ-ing homophobic and transphobic commentsdirected at LGBTQ+ individuals. Counter-narratives provide a constructive way to re-spond to harmful speech by promoting em-pathy, factual clarification, and respectful di-alogue.In this work, we participate in the Shared Taskon Counter-Narrative Generation on Homopho-bic and Transphobic Comments at LT-EDI @ACL 2026. We adopt a zero-shot promptingapproach using large language models accessedthrough publicly available AI tools, includingGPT-4o, Gemini 1.5 Pro, and Llama-3 SonarLarge via Perplexity AI. Instead of traininga task-specific model, we design a structuredprompt that guides the models to generate re-spectful, concise, and contextually appropriatecounter-narratives.Experiments were conducted on English andTamil comments provided by the organiz-ers. Results demonstrate that prompt-basedgeneration can produce meaningful multilin-gual counter-narratives without additional train-ing. Our approach highlights the potential oflarge language models as lightweight tools forcounter-speech generation in multilingual on-line environments.
%U https://aclanthology.org/2026.ltedi-1.21/
%P 188-192
Markdown (Informal)
[JusticeBots@LT-EDI 2026: Prompt-Based Counter-Narrative Generation for Homophobia and Transphobia Comments](https://aclanthology.org/2026.ltedi-1.21/) (Pranesh et al., LTEDI 2026)
ACL