@inproceedings{lekkala-etal-2025-nits,
    title = "nits{\_}teja{\_}srikar at {G}en{AI} Detection Task 2: Distinguishing Human and {AI}-Generated Essays Using Machine Learning and Transformer Models",
    author = "Lekkala, Sai Teja  and
      Yadagiri, Annepaka  and
      Vardhan, Mangadoddi Srikar  and
      Pakray, Partha",
    editor = "Alam, Firoj  and
      Nakov, Preslav  and
      Habash, Nizar  and
      Gurevych, Iryna  and
      Chowdhury, Shammur  and
      Shelmanov, Artem  and
      Wang, Yuxia  and
      Artemova, Ekaterina  and
      Kutlu, Mucahid  and
      Mikros, George",
    booktitle = "Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Conference on Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2025.genaidetect-1.30/",
    pages = "278--283",
    abstract = "This 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."
}