On the Salience of Low-Probability Tokens for AI-Generated Text Detection: A Multiscale Uncertainty Perspective
arXiv:2606.02158v1 Announce Type: new Abstract: AI-generated text increasingly blends with human writing, raising practical risks such as misinformation, academic misuse, and corpora contamination. While statistical detectors are appealing for efficiency and generalization, they suffer from two key limitations. (i) Boilerplate dominance, boilerplate tokens shared across human and LLM writing can overwhelm discriminative signals. (ii) Brittle point estimates, relying on a single probability score yields unstable decisions under adversarial manipulations. To address these issues, we propose Unce
The proliferation of increasingly sophisticated AI-generated text necessitates continuous improvement in detection mechanisms to address growing risks.
Improved AI-generated text detection is crucial for mitigating widespread misinformation, academic integrity issues, and data contamination, which could undermine trust in digital information.
The proposed method offers a more robust and generalized approach to identifying AI-generated content by focusing on low-probability tokens and multiscale uncertainty, moving beyond brittle point estimates.
- · AI content detectors
- · Academic institutions
- · Social media platforms
- · News organizations
- · Misinformation actors
- · Automated spam operations
- · Users engaging in academic plagiarism
More accurate and resilient AI-generated text detection tools will reduce the effectiveness of deceptive AI content.
The increased difficulty of passing off AI-generated content as human may foster a renewed focus on human-authored originality and credibility.
This could lead to an adversarial arms race between generation and detection, driving further innovation in both AI text creation and anti-detection techniques.
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Read at arXiv cs.CL