When Less is More: Understanding When Token Filtering Helps and Fails in AI-generated Text Detection
The rapid advancement of large language models (LLMs) has made AI-generated text detection increasingly critical. Existing zero-shot detectors assume that more token-level evidence leads to more reliable detection. However, our empirical study challenges this consensus: fewer tokens sometimes work better, retaining only 40% can yield optimal performance, yet this benefit is not universal. Using the Entropy Gap Score (EGS), we introduce top-$k$ cumulative probability filtering as a diagnostic probe. Across three representative settings, filtering exhibits strikingly different behaviors. We analyze EGS via typical set theory and quantify its dynamics through entropy calibration and distribu...
| Authors | Xiaoyang Han, Lvxiaowei Xu, Ming Cai |
|---|---|
| Primary category | cs.CL |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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