arXiv:2605.22654v1 Announce Type: new Abstract: Previous detection studies have shown that LLMs cannot be effectively used as detectors, but these studies have not addressed modern Chinese poetry. Moreover, no relevant research has explored the performance of LLMs in detecting modern Chinese poetry. This paper evaluates and enhances the performance of LLMs as detectors for modern Chinese poetry, and proposes an image-semantic guided poetry detection method. Compared with traditional detection approaches, our method innovatively incorporates images that reflect the content of the poetry. Throug
The proliferation of AI-generated content across various mediums necessitates robust detection methods, particularly for nuanced cultural expressions like poetry.
The ability to accurately detect AI-generated content, especially within complex linguistic and cultural contexts, is crucial for maintaining authenticity and creative integrity.
This research introduces a novel multi-modal approach for AI-generated poetry detection, moving beyond traditional text-only methods by incorporating image semantics.
- · AI content authenticity platforms
- · Digital forensics specialists
- · Cultural preservation initiatives
- · Researchers in AI safety and ethics
- · Malicious actors generating undetectable AI content
- · Platforms without advanced AI detection capabilities
Improved detection of AI-generated poetry, especially in challenging languages like Chinese.
Development of more sophisticated multi-modal AI detection tools for various forms of AI-generated content.
Enhanced trust in digital content, potentially fostering new forms of human-AI collaboration in creative fields where authenticity can be verified.
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Read at arXiv cs.CL