Efficient Detection of LLM-generated Texts with a Bayesian Surrogate
  Model
Zhijie Deng Hongcheng Gao Yibo Miao Hao Zhang
The detection of machine-generated text, especially from large language
models (LLMs), is crucial in preventing serious social problems resulting from
their misuse. Some methods train dedicated detectors on specific datasets but
fall short in generalizing to unseen test data, while other zero-shot ones
often yield suboptimal performance. Although the recent DetectGPT has shown
promising detection performance, it suffers from significant inefficiency
issues, as detecting a single candidate requires scoring hundreds of its
perturbations with the source LLM. This paper aims to bridge this gap.
Technically, we propose to incorporate a Bayesian surrogate model, which allows
us to select typical samples based on Bayesian uncertainty and interpolate
scores from typical samples to other ones, to improve query efficiency. Our
empirical results demonstrate that our method significantly outperforms
existing approaches under a low query budget. Notably, our method achieves
similar performance with up to 2 times fewer queries than DetectGPT and 3.7%
higher AUROC at a query number of 5.