Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model
CoRR(2023)
Abstract
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 querying the source LLM with
hundreds of its perturbations. This paper aims to bridge this gap. Concretely,
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 samples, to improve query efficiency. Empirical
results demonstrate that our method significantly outperforms existing
approaches under a low query budget. Notably, when detecting the text generated
by LLaMA family models, our method with just 2 or 3 queries can outperform
DetectGPT with 200 queries.
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