nits_teja_srikar at GenAI Detection Task 2: Distinguishing Human and AI-Generated Essays Using Machine Learning and Transformer Models

AbstractThis paper presents models to differentiate between human-written and AI-generated essays, addressing challenges posed by advanced AI models like ChatGPT and Claude. Using a structured dataset, we fine-tune multiple machine learning models, including XGBoost and Logistic Regression, along with ensemble learning and k-fold cross-validation. The dataset is processed through TF-IDF vectorization, followed by text cleaning, lemmatization, stemming, and part-of-speech tagging before training. Our team nits_teja_srikar achieves high accuracy, with DistilBERT performing at 77.3% accuracy, standing at 20th position for English, and XLM-RoBERTa excelling in Arabic at 92.2%, standing at 14th position in the official leaderboard, demonstrating the model’s potential for real-world applications.