@inproceedings{tran-nam-2025-l3i,
    title = "L3i++ at {G}en{AI} Detection Task 1: Can Label-Supervised {LL}a{MA} Detect Machine-Generated Text?",
    author = "Tran, Hanh Thi Hong  and
      Nam, Nguyen Tien",
    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-1.13/",
    pages = "155--160",
    abstract = "The widespread use of large language models (LLMs) influences different social media and educational contexts through the overwhelming generated text with a certain degree of coherence. To mitigate their potential misuse, this paper explores the feasibility of finetuning LLaMA with label supervision (named LS-LLaMA) in unidirectional and bidirectional settings, to discriminate the texts generated by machines and humans in monolingual and multilingual corpora. Our findings show that unidirectional LS-LLaMA outperformed the sequence language models as the benchmark by a large margin. Our code is publicly available at https://github.com/honghanhh/llama-as-a-judge."
}