Papers with AI-text detection
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. |
Navigating the Shadows: Unveiling Effective Disturbances for Modern AI Content Detectors (2024.acl-long)
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| Challenge: | Recent research indicates that AI-text detection systems lack robustness and struggle to effectively differentiate perturbed texts. |
| Approach: | They propose to evaluate the robustness of current detection systems by using black-box text perturbation methods and adversarial learning experiments. |
| Outcome: | The proposed methods assess the robustness of current detection models across perturbation granularities and the impact of perturbation data augmentation on the robustity of AI-text detectors. |
TempParaphraser: “Heating Up” Text to Evade AI-Text Detection through Paraphrasing (2025.emnlp-main)
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| Challenge: | Existing detectors that perform well on benchmark datasets have weaknesses that can be exploited to manipulate AI-text. |
| Approach: | They propose a framework that simulates high-temperature sampling effects through multiple normal-temperaturing generations, effectively evading detection. |
| Outcome: | The proposed framework reduces detector accuracy by an average of 82.5% while preserving high text quality. |