@inproceedings{yadagiri-etal-2025-cnlp-nits,
    title = "{CNLP}-{NITS}-{PP} at {G}en{AI} Detection Task 2: Leveraging {D}istil{BERT} and {XLM}-{R}o{BERT}a for Multilingual {AI}-Generated Text Detection",
    author = "Yadagiri, Annepaka  and
      Krishna, Reddi Mohana  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/fix-sig-urls/2025.genaidetect-1.34/",
    pages = "307--311",
    abstract = "In today{'}s digital landscape, distinguishing between human-authored essays and content generated by advanced Large Language Models such as ChatGPT, GPT-4, Gemini, and LLaMa has become increasingly complex. This differentiation is essential across sectors like academia, cybersecurity, social media, and education, where the authenticity of written material is often crucial. Addressing this challenge, the COLING 2025 competition introduced Task 2, a binary classification task to separate AI-generated text from human-authored content. Using a benchmark dataset for English and Arabic, developing a methodology that fine-tuned various transformer-based neural networks, including CNN-LSTM, RNN, Bi-GRU, BERT, DistilBERT, GPT-2, and RoBERTa. Our Team CNLP-NITS-PP achieved competitive performance through meticulous hyperparameter optimization, reaching a Recall score of 0.825. Specifically, we ranked 18th in the English sub-task A with an accuracy of 0.77 and 20th in the Arabic sub-task B with an accuracy of 0.59. These results underscore the potential of transformer-based models in academic settings to detect AI-generated content effectively, laying a foundation for more advanced methods in essay authenticity verification."
}