arXiv:2606.18946v1 Announce Type: new Abstract: Sentence-level AI-generated text detection (S-AGTD) for hybrid documents, where humans and LLMs co-author one text, faces two gaps: existing methods classify each sentence in isolation, discarding inter-sentence dependencies, and existing benchmarks omit the newest generation of generators. We construct MOSAIC, a benchmark of 16,000 hybrid documents over PubMed and XSum, generated by DeepSeek-V3.2 and Kimi K2 under stringent quality controls including a perplexity-consistency filter absent from prior benchmarks. We recast S-AGTD as structured pre
The proliferation of advanced LLMs and hybrid human-AI document creation necessitates more sophisticated detection methods, as current tools are insufficient.
Improved detection of AI-generated text is critical for maintaining authenticity, combating misinformation, and developing robust AI attribution tools.
The ability to accurately identify AI-generated content within hybrid documents will improve transparency and accountability in information creation, particularly in fields like scientific research.
- · AI ethicists
- · Content verification platforms
- · Academic publishers
- · LLM developers
- · Malicious misinformation actors
- · Automated spam generators
New benchmarks and methodologies will lead to more robust AI-generated text detection systems.
Increased trust in digital content as the provenance of text becomes clearer, reducing the impact of undetectable synthetic media.
The development of 'AI-proof' content creation and verification standards, shaping future digital information ecosystems.
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Read at arXiv cs.CL