@inproceedings{hasan-etal-2025-ecstasy,
title = "Ecstasy at {BLP}-2025 Task 1: {TF}-{IDF} Informed Prompt Engineering with {L}o{RA} Fine-tuning for {B}angla Hate Speech Detection",
author = "Hasan, Kazi Reyazul and
Musarrat, Mubasshira and
Adnan, Muhammad Abdullah",
editor = "Alam, Firoj and
Kar, Sudipta and
Chowdhury, Shammur Absar and
Hassan, Naeemul and
Prince, Enamul Hoque and
Tasnim, Mohiuddin and
Rony, Md Rashad Al Hasan and
Rahman, Md Tahmid Rahman",
booktitle = "Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.banglalp-1.52/",
pages = "551--555",
ISBN = "979-8-89176-314-2",
abstract = "We present a hybrid approach for Bangla hate speech detection that combines linguistic analysis with neural fine tuning. Our method first identifies category specific keywords using TF-IDF analysis on 35,522 training samples. These keywords then inform prompt engineering for Llama 3.1 8B model fine tuned with LoRA adapters. We incorporate distinctive Bangla terms directly into classification prompts to guide the model understanding of hate speech patterns. Our system achieved top 5 rankings across all three BLP 2025 Task 1 subtasks including hate type classification, target identification, and multi task prediction. The approach proved particularly effective for culturally specific hate speech patterns unique to Bangla social media discourse."
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<abstract>We present a hybrid approach for Bangla hate speech detection that combines linguistic analysis with neural fine tuning. Our method first identifies category specific keywords using TF-IDF analysis on 35,522 training samples. These keywords then inform prompt engineering for Llama 3.1 8B model fine tuned with LoRA adapters. We incorporate distinctive Bangla terms directly into classification prompts to guide the model understanding of hate speech patterns. Our system achieved top 5 rankings across all three BLP 2025 Task 1 subtasks including hate type classification, target identification, and multi task prediction. The approach proved particularly effective for culturally specific hate speech patterns unique to Bangla social media discourse.</abstract>
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%0 Conference Proceedings
%T Ecstasy at BLP-2025 Task 1: TF-IDF Informed Prompt Engineering with LoRA Fine-tuning for Bangla Hate Speech Detection
%A Hasan, Kazi Reyazul
%A Musarrat, Mubasshira
%A Adnan, Muhammad Abdullah
%Y Alam, Firoj
%Y Kar, Sudipta
%Y Chowdhury, Shammur Absar
%Y Hassan, Naeemul
%Y Prince, Enamul Hoque
%Y Tasnim, Mohiuddin
%Y Rony, Md Rashad Al Hasan
%Y Rahman, Md Tahmid Rahman
%S Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-314-2
%F hasan-etal-2025-ecstasy
%X We present a hybrid approach for Bangla hate speech detection that combines linguistic analysis with neural fine tuning. Our method first identifies category specific keywords using TF-IDF analysis on 35,522 training samples. These keywords then inform prompt engineering for Llama 3.1 8B model fine tuned with LoRA adapters. We incorporate distinctive Bangla terms directly into classification prompts to guide the model understanding of hate speech patterns. Our system achieved top 5 rankings across all three BLP 2025 Task 1 subtasks including hate type classification, target identification, and multi task prediction. The approach proved particularly effective for culturally specific hate speech patterns unique to Bangla social media discourse.
%U https://aclanthology.org/2025.banglalp-1.52/
%P 551-555
Markdown (Informal)
[Ecstasy at BLP-2025 Task 1: TF-IDF Informed Prompt Engineering with LoRA Fine-tuning for Bangla Hate Speech Detection](https://aclanthology.org/2025.banglalp-1.52/) (Hasan et al., BanglaLP 2025)
ACL