arXiv:2510.22874v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has led to increasingly human-like AI-generated text, raising concerns about content authenticity, misinformation, and trustworthiness. Addressing the challenge of reliably detecting AI-generated text and attributing it to specific models requires large-scale, diverse, and well-annotated datasets. In this work, we present a comprehensive dataset comprising over 73,193 text samples that combine authentic New York Times articles with synthetic versions generated by multiple state-of-the-art
The rapid advancement and accessibility of large language models are making AI-generated text increasingly indistinguishable from human text, necessitating robust detection methods immediately.
Reliably detecting AI-generated text is crucial for preserving content authenticity, combating misinformation, and maintaining trust in public information, which impacts various sectors from media to education.
The availability of a large, diverse, and well-annotated dataset will accelerate research and development of more accurate AI-generated text detection tools, potentially shifting the arms race between generation and detection.
- · AI content verification platforms
- · Academic researchers in NLP
- · News organizations combating misinformation
- · Malicious actors using AI for disinformation
- · Platforms struggling with content moderation
- · Early, unsophisticated AI detection methods
Improved AI text detection allows platforms to more effectively label or filter AI-generated content.
Public trust in online information is partially restored as the provenance of text becomes clearer.
The development of 'AI watermarking' or provable originality for human-generated content becomes a new industry standard to circumvent detection challenges.
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Read at arXiv cs.CL