Crafting Tomorrow's Headlines: Neural News Generation and Detection in English, Turkish, Hungarian, and Persian
<|reference_start|>Crafting Tomorrow's Headlines: Neural News Generation and Detection in English, Turkish, Hungarian, and Persian: In the era dominated by information overload and its facilitation with Large Language Models (LLMs), the prevalence of misinformation poses a significant threat to public discourse and societal well-being. A critical concern at present involves the identification of machine-generated news. In this work, we take a significant step by introducing a benchmark dataset designed for neural news detection in four languages: English, Turkish, Hungarian, and Persian. The dataset incorporates outputs from multiple multilingual generators (in both, zero-shot and fine-tuned setups) such as BloomZ, LLaMa-2, Mistral, Mixtral, and GPT-4. Next, we experiment with a variety of classifiers, ranging from those based on linguistic features to advanced Transformer-based models and LLMs prompting. We present the detection results aiming to delve into the interpretablity and robustness of machine-generated texts detectors across all target languages.<|reference_end|>
@article{üyük2024crafting,
    title={Crafting Tomorrow's Headlines: Neural News Generation and Detection in
  English, Turkish, Hungarian, and Persian},
    author={Cem "Uy"uk, Danica Rov'o, Shaghayegh Kolli, Rabia Varol, Georg
  Groh, Daryna Dementieva},
    journal={arXiv preprint arXiv:2408.10724},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10724},
    primaryClass={cs.CL}
}