Detecting AI-generated text has become in- creasingly prominent. This paper presents our solution for the DAIGenC Task 1 Subtask 2, where we address the challenge of distin- guishing human-authored text from machine- generated content, especially in multilingual contexts. We introduce Multi-Task Detection (MLDet), a model that leverages Cross-Lingual Adaptation and Model Generalization strate- gies for Multilingual Machine-Generated Text (MGT) detection. By combining language- specific embeddings with fusion techniques, MLDet creates a unified, language-agnostic feature representation, enhancing its ability to generalize across diverse languages and mod- els. Our approach demonstrates strong perfor- mance, achieving macro and micro F1 scores of 0.7067 and 0.7187, respectively, and ranking 15th in the competition1. We also evaluate our model across datasets generated by different distinct models in many languages, showcasing its robustness in multilingual and cross-model scenarios.

Detecting AI-generated text in the field of academia is becoming very prominent. This paper presents a solution for Task 2: AI vs. Hu- man – Academic Essay Authenticity Challenge in the COLING 2025 DAIGenC Workshop 1. The rise of Large Language models (LLMs) like ChatGPT has posed significant challenges to academic integrity, particularly in detecting AI-generated essays. To address this, we pro- pose a fusion model that combines pre-trained language model embeddings with stylometric and linguistic features. Our approach, tested on both English and Arabic, utilizes adaptive training and attention mechanisms to enhance F1 scores, address class imbalance, and capture linguistic nuances across languages. This work advances multilingual solutions for detecting AI-generated text in academia.

Machine-generated text (MGT) detection has gained critical importance in the era of large language models, especially for maintaining trust in multilingual and cross-domain applica- tions. This paper presents Task 3 Subtask B: Adversarial Cross-Domain MGT Detection for in the COLING 2025 DAIGenC Workshop. Task 3 emphasizes the complexity of detecting AI-generated text across eight domains, eleven generative models, and four decoding strate- gies, with an added challenge of adversarial manipulation. We propose a robust detection framework transformer embeddings utilizing Domain-Adversarial Neural Networks (DANN) to address domain variability and adversarial robustness. Our model demonstrates strong performance in identifying AI-generated text under adversarial conditions while highlighting condition scope of future improvement.