Abstract: Deployed real-world machine learning applications are often subject to
uncontrolled and even potentially malicious inputs. Those out-of-domain inputs
can lead to unpredictable outputs and sometimes catastrophic safety issues.
Prior studies on out-of-domain detection require in-domain task labels and are
limited to supervised classification scenarios. Our work tackles the problem of
detecting out-of-domain samples with only unsupervised in-domain data. We
utilize the latent representations of pre-trained transformers and propose a
simple yet effective method to transform features across all layers to
construct out-of-domain detectors efficiently. Two domain-specific fine-tuning
approaches are further proposed to boost detection accuracy. Our empirical
evaluations of related methods on two datasets validate that our method greatly
improves out-of-domain detection ability in a more general scenario.