Papers with discriminative adaptation stage

1 papers
Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection (2026.acl-long)

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Challenge: a rapid proliferation of large language models (LLMs) generate text that increasingly resembles human writing . this makes it difficult to capture subtle cues that distinguish AI-generated content from human-written content .
Approach: They propose a framework that disentangles AI-detection semantics from generator-aware artifacts by latent encoding and perturbation-based regularization.
Outcome: The proposed framework disentangles AI-detection semantics from generator-aware artifacts on 20 representative LLMs across 7 categories.

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