DWT-Fusion: A Signal-Based Framework for Training-Free LLM-Generated Text Detection
Detecting LLM-generated text remains challenging under zero-shot and training-free conditions, especially when detectors must generalize across datasets, domains, and unseen generators. While existing training-free approaches exploit language-model statistics as detection signals, they typically characterize a text through global measures that summarize overall model behavior. Consequently, potentially informative local and multiscale variations in token-level predictability may remain underutilized. Motivated by this observation, we introduce DWT-Fusion, a training-free signal-based framework for detecting LLM-generated text using discrete wavelet analysis of token-level log-probability ...
| Authors | Mehmet Batuhan Özdaş, Murat Osmanoğlu |
|---|---|
| Primary category | cs.CL |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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