A disentangled representation framework for AI-text detection improves generalization to unseen generators with up to 24.2% accuracy gain on the MAGE benchmark covering 20 LLMs.
Howkgpt: Investigating the detection of chatgpt-generated university student homework through context-aware perplexity analysis
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GigaCheck detects LLM-generated text at both document and span levels by combining fine-tuned language-model embeddings with a DETR-like architecture that treats generated intervals as detectable objects.
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Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection
A disentangled representation framework for AI-text detection improves generalization to unseen generators with up to 24.2% accuracy gain on the MAGE benchmark covering 20 LLMs.
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GigaCheck: Detecting LLM-generated Content via Object-Centric Span Localization
GigaCheck detects LLM-generated text at both document and span levels by combining fine-tuned language-model embeddings with a DETR-like architecture that treats generated intervals as detectable objects.