@inproceedings{k-etal-2025-nlp-goats,
title = "{NLP}{\_}goats{\_}{D}ravidian{L}ang{T}ech{\_}2025{\_}{\_}{D}etecting{\_}{AI}{\_}{W}ritten{\_}{R}eviews{\_}for{\_}{C}onsumer{\_}{T}rust",
author = "K, Srihari V and
Vaidyanathan, Vijay Karthick and
U, Mugilkrishna D and
Durairaj, Thenmozhi",
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-1.65/",
doi = "10.18653/v1/2025.dravidianlangtech-1.65",
pages = "366--370",
ISBN = "979-8-89176-228-2",
abstract = "The rise of AI-generated content has introduced challenges in distinguishing machine-generated text from human-written text, particularly in low-resource languages. The identification of artificial intelligence (AI)-based reviews is of significant importance to preserve trust and authenticity on online platforms. The Shared Task on Detecting AI-Generated Product Reviews in Dravidian languages deals with the task of detecting AI-generated and human-written reviews in Tamil and Malayalam. To solve this problem, we specifically fine-tuned mBERT for binary classification. Our system achieved 10th place in Tamil with a macro F1-score of 0.90 and 28th place in Malayalam with a macro F1-score of 0.68, as reported by the NAACL 2025 organizers. The findings demonstrate the complexity involved in the separation of AI-derived text from human-authored writing, with a call for continued advances in detection methods."
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%0 Conference Proceedings
%T NLP_goats_DravidianLangTech_2025__Detecting_AI_Written_Reviews_for_Consumer_Trust
%A K, Srihari V.
%A Vaidyanathan, Vijay Karthick
%A U, Mugilkrishna D.
%A Durairaj, Thenmozhi
%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 k-etal-2025-nlp-goats
%X The rise of AI-generated content has introduced challenges in distinguishing machine-generated text from human-written text, particularly in low-resource languages. The identification of artificial intelligence (AI)-based reviews is of significant importance to preserve trust and authenticity on online platforms. The Shared Task on Detecting AI-Generated Product Reviews in Dravidian languages deals with the task of detecting AI-generated and human-written reviews in Tamil and Malayalam. To solve this problem, we specifically fine-tuned mBERT for binary classification. Our system achieved 10th place in Tamil with a macro F1-score of 0.90 and 28th place in Malayalam with a macro F1-score of 0.68, as reported by the NAACL 2025 organizers. The findings demonstrate the complexity involved in the separation of AI-derived text from human-authored writing, with a call for continued advances in detection methods.
%R 10.18653/v1/2025.dravidianlangtech-1.65
%U https://aclanthology.org/2025.dravidianlangtech-1.65/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.65
%P 366-370
Markdown (Informal)
[NLP_goats_DravidianLangTech_2025__Detecting_AI_Written_Reviews_for_Consumer_Trust](https://aclanthology.org/2025.dravidianlangtech-1.65/) (K et al., DravidianLangTech 2025)
ACL
- Srihari V K, Vijay Karthick Vaidyanathan, Mugilkrishna D U, and Thenmozhi Durairaj. 2025. NLP_goats_DravidianLangTech_2025__Detecting_AI_Written_Reviews_for_Consumer_Trust. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 366–370, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.