AI-generated text now matches human writing so well that telling them apart is very difficult. Our CIC-NLP team submits results for the DravidianLangTech@NAACL 2025 shared task to reveal AI-generated product reviews in Dravidian languages. We performed a binary classification task with XLM-RoBERTa-Base using the DravidianLangTech@NAACL 2025 datasets offered by the event organizers. Through training the model correctly, our tests could tell between human and AI-generated reviews with scores of 0.96 for Tamil and 0.88 for Malayalam in the evaluation test set. This paper presents detailed information about preprocessing, model architecture, hyperparameter fine-tuning settings, the experimental process, and the results. The source code is available on GitHub1.

Misinformation is a growing problem for technologycompanies and for society. Although there exists a large body of related work on identifying fake news in predominantlyresource languages, there is unfortunately a lack of such studies in low-resource languages (LRLs). Because corpora and annotated data are scarce in LRLs, the identification of false information remains at an exploratory stage. Fake news detection is critical in this digital era to avoid spreading misleading information. This research work presents an approach to Detect Fake News in Dravidian Languages. Our team CIC-NLP work primarily targets Task 1 which involves identifying whether a given social platform news is original or fake. For fake news detection (FND) problem, we used mBERT model and utilized the dataset that was provided by the organizers of the workshop. In this work, we describe our findings and the results of the proposed method. Our mBERT model achieved an F1 score of 0.853.

This study goes into our team’s active participation in the Hate and Offensive Language Detection in Telugu Codemixed Text (HOLDTelugu) shared task, which is an essential component of the DravidianLangTech@EACL 2024 workshop. The ultimate goal of this collaborative work is to push the bounds of hate speech recognition, especially tackling the issues given by codemixed text in Telugu, where English blends smoothly. Our inquiry offers a complete evaluation of the task’s aims, the technique used, and the precise achievements obtained by our team, providing a full insight into our contributions to this crucial linguistic and technical undertaking.