@inproceedings{sidibomma-etal-2025-llmsagainsthate,
title = "{LLM}s{A}gainst{H}ate@{NLU} of {D}evanagari Script Languages 2025: Hate Speech Detection and Target Identification in {D}evanagari Languages via Parameter Efficient Fine-Tuning of {LLM}s",
author = "Sidibomma, Rushendra and
Patwa, Pransh and
Patwa, Parth and
Chadha, Aman and
Jain, Vinija and
Das, Amitava",
editor = "Sarveswaran, Kengatharaiyer and
Vaidya, Ashwini and
Krishna Bal, Bal and
Shams, Sana and
Thapa, Surendrabikram",
booktitle = "Proceedings of the First Workshop on Challenges in Processing South Asian Languages (CHiPSAL 2025)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2025.chipsal-1.34/",
pages = "301--307",
abstract = "The detection of hate speech has become increasingly important in combating online hostility and its real-world consequences. Despite recent advancements, there is limited research addressing hate speech detection in Devanagari-scripted languages, where resources and tools are scarce. While large language models (LLMs) have shown promise in language-related tasks, traditional fine-tuning approaches are often infeasible given the size of the models. In this paper, we propose a Parameter Efficient Fine tuning (PEFT) based solution for hate speech detection and target identification. We evaluate multiple LLMs on the Devanagari dataset provided by Thapa et al. (2025), which contains annotated instances in 2 languages - Hindi and Nepali. The results demonstrate the efficacy of our approach in handling Devanagari-scripted content. Code will be made publicly available on GitHub following acceptance."
}
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%0 Conference Proceedings
%T LLMsAgainstHate@NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs
%A Sidibomma, Rushendra
%A Patwa, Pransh
%A Patwa, Parth
%A Chadha, Aman
%A Jain, Vinija
%A Das, Amitava
%Y Sarveswaran, Kengatharaiyer
%Y Vaidya, Ashwini
%Y Krishna Bal, Bal
%Y Shams, Sana
%Y Thapa, Surendrabikram
%S Proceedings of the First Workshop on Challenges in Processing South Asian Languages (CHiPSAL 2025)
%D 2025
%8 January
%I International Committee on Computational Linguistics
%C Abu Dhabi, UAE
%F sidibomma-etal-2025-llmsagainsthate
%X The detection of hate speech has become increasingly important in combating online hostility and its real-world consequences. Despite recent advancements, there is limited research addressing hate speech detection in Devanagari-scripted languages, where resources and tools are scarce. While large language models (LLMs) have shown promise in language-related tasks, traditional fine-tuning approaches are often infeasible given the size of the models. In this paper, we propose a Parameter Efficient Fine tuning (PEFT) based solution for hate speech detection and target identification. We evaluate multiple LLMs on the Devanagari dataset provided by Thapa et al. (2025), which contains annotated instances in 2 languages - Hindi and Nepali. The results demonstrate the efficacy of our approach in handling Devanagari-scripted content. Code will be made publicly available on GitHub following acceptance.
%U https://aclanthology.org/2025.chipsal-1.34/
%P 301-307
Markdown (Informal)
[LLMsAgainstHate@NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs](https://aclanthology.org/2025.chipsal-1.34/) (Sidibomma et al., CHiPSAL 2025)
ACL