Ten Words Only Still Help: Improving Black-Box AI-Generated Text
  Detection via Proxy-Guided Efficient Re-Sampling
Yuhui Shi Qiang Sheng Juan Cao Hao Mi Beizhe Hu Danding Wang
With the rapidly increasing application of large language models (LLMs),
their abuse has caused many undesirable societal problems such as fake news,
academic dishonesty, and information pollution. This makes AI-generated text
(AIGT) detection of great importance. Among existing methods, white-box methods
are generally superior to black-box methods in terms of performance and
generalizability, but they require access to LLMs' internal states and are not
applicable to black-box settings. In this paper, we propose to estimate word
generation probabilities as pseudo white-box features via multiple re-sampling
to help improve AIGT detection under the black-box setting. Specifically, we
design POGER, a proxy-guided efficient re-sampling method, which selects a
small subset of representative words (e.g., 10 words) for performing multiple
re-sampling in black-box AIGT detection. Experiments on datasets containing
texts from humans and seven LLMs show that POGER outperforms all baselines in
macro F1 under black-box, partial white-box, and out-of-distribution settings
and maintains lower re-sampling costs than its existing counterparts.