dwt-fusion-d99685d6·1 events·first seen Aliases: DWT-Fusion
Researchers introduce DWT-Fusion, a training-free method for detecting LLM-generated text that applies discrete wavelet transforms to token-level log-probability sequences from a proxy language model, capturing local and multiscale predictability patterns rather than global statistics. The framework evaluates four voting ensemble variants and is tested on HC3, M4, and MAGE benchmarks using GPT-Neo-2.7B, GPT-J-6B, Falcon-7B, and LLaMA-3-8B as proxy models. Best ensemble results achieve AUROC of 0.9919, 0.8477, and 0.7471 on the three benchmarks respectively. The approach is notable for requiring no supervised training while remaining interpretable.