@inproceedings{stuhlinger-winkler-2024-tuecicl,
    title = "{T}ue{CICL} at {S}em{E}val-2024 Task 8: Resource-efficient approaches for machine-generated text detection",
    author = "Stuhlinger, Daniel  and
      Winkler, Aron",
    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/fix-sig-urls/2024.semeval-1.227/",
    doi = "10.18653/v1/2024.semeval-1.227",
    pages = "1597--1601",
    abstract = "Recent developments in the field of NLP have brought large language models (LLMs) to the forefront of both public and research attention. As the use of language generation technologies becomes more widespread, the problem arises of determining whether a given text is machine generated or not. Task 8 at SemEval 2024 consists of a shared task with this exact objective. Our approach aims at developing models and strategies that strike a good balance between performance and model size. We show that it is possible to compete with large transformer-based solutions with smaller systems."
}