@inproceedings{onajol-etal-2024-lora,
title = "{L}o{RA} adapter weight tuning with multi-task learning for Faux-Hate detection",
author = "Onajol, Abhinandan and
Gani, Varun and
Marakatti, Praneeta and
Malwankar, Bhakti and
Biradar, Shankar",
editor = "Biradar, Shankar and
Reddy, Kasu Sai Kartheek and
Saumya, Sunil and
Akhtar, Md. Shad",
booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate)",
month = dec,
year = "2024",
address = "AU-KBC Research Centre, Chennai, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2024.icon-fauxhate.11/",
pages = "56--60",
abstract = "Detecting misinformation and harmful language in bilingual texts, particularly those com-bining Hindi and English, poses considerabledifficulties. The intricacies of mixed-languagecontent and limited available resources compli-cate this task even more. The proposed workfocuses on unraveling deceptive stories thatpropagate hate. We have developed an inno-vative attention-weight-tuned LoRA Adopter-based model for such Faux-Hate content de-tection. This work is conducted as a partof the ICON 2024 shared task on DecodingFake narratives in spreading Hateful stories.The LoRA-enhanced architecture secured 13thplace among the participating teams for TaskA."
}
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<abstract>Detecting misinformation and harmful language in bilingual texts, particularly those com-bining Hindi and English, poses considerabledifficulties. The intricacies of mixed-languagecontent and limited available resources compli-cate this task even more. The proposed workfocuses on unraveling deceptive stories thatpropagate hate. We have developed an inno-vative attention-weight-tuned LoRA Adopter-based model for such Faux-Hate content de-tection. This work is conducted as a partof the ICON 2024 shared task on DecodingFake narratives in spreading Hateful stories.The LoRA-enhanced architecture secured 13thplace among the participating teams for TaskA.</abstract>
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%0 Conference Proceedings
%T LoRA adapter weight tuning with multi-task learning for Faux-Hate detection
%A Onajol, Abhinandan
%A Gani, Varun
%A Marakatti, Praneeta
%A Malwankar, Bhakti
%A Biradar, Shankar
%Y Biradar, Shankar
%Y Reddy, Kasu Sai Kartheek
%Y Saumya, Sunil
%Y Akhtar, Md. Shad
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F onajol-etal-2024-lora
%X Detecting misinformation and harmful language in bilingual texts, particularly those com-bining Hindi and English, poses considerabledifficulties. The intricacies of mixed-languagecontent and limited available resources compli-cate this task even more. The proposed workfocuses on unraveling deceptive stories thatpropagate hate. We have developed an inno-vative attention-weight-tuned LoRA Adopter-based model for such Faux-Hate content de-tection. This work is conducted as a partof the ICON 2024 shared task on DecodingFake narratives in spreading Hateful stories.The LoRA-enhanced architecture secured 13thplace among the participating teams for TaskA.
%U https://aclanthology.org/2024.icon-fauxhate.11/
%P 56-60
Markdown (Informal)
[LoRA adapter weight tuning with multi-task learning for Faux-Hate detection](https://aclanthology.org/2024.icon-fauxhate.11/) (Onajol et al., ICON 2024)
ACL
- Abhinandan Onajol, Varun Gani, Praneeta Marakatti, Bhakti Malwankar, and Shankar Biradar. 2024. LoRA adapter weight tuning with multi-task learning for Faux-Hate detection. In Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate), pages 56–60, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).