This study presents a hybrid model integrating TamilXLM-RoBERTa and MalayalamXLM-RoBERTa with BiLSTM and attention mechanisms to classify AI-generated and human-written product reviews in Tamil and Malayalam. The model employs a transliteration-based fine-tuning strategy, effectively handling native, Romanized, and mixed-script text. Despite being trained on a relatively small portion of data, our approach demonstrates strong performance in distinguishing AI-generated content, achieving competitive macro F1 scores in the DravidianLangTech 2025 shared task. The proposed method showcases the effectiveness of multilingual transformers and hybrid architectures in tackling low-resource language challenges.

This research investigates abusive comment detection in Tamil and Malayalam, focusing on code-mixed, multilingual social media text. A hybrid Attention BiLSTM-XLM-RoBERTa model was utilized, combining fine-tuned embeddings, sequential dependencies, and attention mechanisms. Despite computational constraints limiting fine-tuning to a subset of the AI4Bharath dataset, the model achieved competitive macro F1-scores, ranking 6th for both Tamil and Malayalam datasets with minor performance differences. The results emphasize the potential of multilingual transformers and the need for further advancements, particularly in addressing linguistic diversity, transliteration complexity, and computational limitations.