@inproceedings{bahad-etal-2024-fine-tuning,
    title = "Fine-tuning Language Models for {AI} vs Human Generated Text detection",
    author = "Bahad, Sankalp  and
      Bhaskar, Yash  and
      Krishnamurthy, Parameswari",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Tayyar Madabushi, Harish  and
      Da San Martino, Giovanni  and
      Rosenthal, Sara  and
      Ros{\'a}, Aiala},
    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.semeval-1.132/",
    doi = "10.18653/v1/2024.semeval-1.132",
    pages = "918--921",
    abstract = "In this paper, we introduce a machine-generated text detection system designed totackle the challenges posed by the prolifera-tion of large language models (LLMs). Withthe rise of LLMs such as ChatGPT and GPT-4,there is a growing concern regarding the po-tential misuse of machine-generated content,including misinformation dissemination. Oursystem addresses this issue by automating theidentification of machine-generated text acrossmultiple subtasks: binary human-written vs.machine-generated text classification, multi-way machine-generated text classification, andhuman-machine mixed text detection. We em-ploy the RoBERTa Base model and fine-tuneit on a diverse dataset encompassing variousdomains, languages, and sources. Throughrigorous evaluation, we demonstrate the effec-tiveness of our system in accurately detectingmachine-generated text, contributing to effortsaimed at mitigating its potential misuse."
}