@inproceedings{aftahee-etal-2025-cuet,
title = "{CUET}{\_}{N}etwork{S}ociety@{D}ravidian{L}ang{T}ech 2025: A Transformer-Based Approach for Detecting {AI}-Generated Product Reviews in Low-Resource {D}ravidian Languages",
author = "Aftahee, Sabik and
Babu, Tofayel Ahmmed and
Ratul, MD Musa Kalimullah and
Hossain, Jawad and
Hoque, Mohammed Moshiul",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.91/",
doi = "10.18653/v1/2025.dravidianlangtech-1.91",
pages = "522--528",
ISBN = "979-8-89176-228-2",
abstract = "E-commerce platforms face growing challenges regarding consumer trust and review authenticity because of the growing number of AI-generated product reviews. Low-resource languages (LRLs) such as Tamil and Malayalam face limited investigation by AI detection techniques because these languages experience constraints from sparse data sources and complex linguistic structures. The research team at CUET{\_}NetworkSociety took part in the AI-Generated Review Detection contest during the DravidianLangTech@NAACL 2025 event to fill this knowledge void. Using a combination of machine learning, deep learning, and transformer-based models, we detected AI-generated and human-written reviews in both Tamil and Malayalam. The developed method employed DistilBERT, which underwent an advanced preprocessing pipeline and hyperparameter optimization using the Transformers library. This approach achieved a Macro F1-score of 0.81 for Tamil (Subtask 1), securing 18th place, and a score of 0.7287 for Malayalam (Subtask 2), ranking 25th."
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%0 Conference Proceedings
%T CUET_NetworkSociety@DravidianLangTech 2025: A Transformer-Based Approach for Detecting AI-Generated Product Reviews in Low-Resource Dravidian Languages
%A Aftahee, Sabik
%A Babu, Tofayel Ahmmed
%A Ratul, MD Musa Kalimullah
%A Hossain, Jawad
%A Hoque, Mohammed Moshiul
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F aftahee-etal-2025-cuet
%X E-commerce platforms face growing challenges regarding consumer trust and review authenticity because of the growing number of AI-generated product reviews. Low-resource languages (LRLs) such as Tamil and Malayalam face limited investigation by AI detection techniques because these languages experience constraints from sparse data sources and complex linguistic structures. The research team at CUET_NetworkSociety took part in the AI-Generated Review Detection contest during the DravidianLangTech@NAACL 2025 event to fill this knowledge void. Using a combination of machine learning, deep learning, and transformer-based models, we detected AI-generated and human-written reviews in both Tamil and Malayalam. The developed method employed DistilBERT, which underwent an advanced preprocessing pipeline and hyperparameter optimization using the Transformers library. This approach achieved a Macro F1-score of 0.81 for Tamil (Subtask 1), securing 18th place, and a score of 0.7287 for Malayalam (Subtask 2), ranking 25th.
%R 10.18653/v1/2025.dravidianlangtech-1.91
%U https://aclanthology.org/2025.dravidianlangtech-1.91/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.91
%P 522-528
Markdown (Informal)
[CUET_NetworkSociety@DravidianLangTech 2025: A Transformer-Based Approach for Detecting AI-Generated Product Reviews in Low-Resource Dravidian Languages](https://aclanthology.org/2025.dravidianlangtech-1.91/) (Aftahee et al., DravidianLangTech 2025)
ACL
- Sabik Aftahee, Tofayel Ahmmed Babu, MD Musa Kalimullah Ratul, Jawad Hossain, and Mohammed Moshiul Hoque. 2025. CUET_NetworkSociety@DravidianLangTech 2025: A Transformer-Based Approach for Detecting AI-Generated Product Reviews in Low-Resource Dravidian Languages. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 522–528, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.