Crafting Tomorrow's Headlines: Neural News Generation and Detection in English, Turkish, Hungarian, and Persian

• URL: http://arxiv.org/abs/2408.10724v3
• Date: Mon, 4 Nov 2024 12:42:18 GMT
• Title: Crafting Tomorrow's Headlines: Neural News Generation and Detection in English, Turkish, Hungarian, and Persian
• Authors: Cem Üyük, Danica Rovó, Shaghayegh Kolli, Rabia Varol, Georg Groh, Daryna Dementieva,
• Abstract summary: We introduce 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. We present the detection results aiming to delve into the interpretablity and robustness of machine-generated texts detectors across all target languages.
• Score: 9.267227655791443
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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