  Out-of-distribution (OOD) detection is a critical task for reliable
predictions over text. Fine-tuning with pre-trained language models has been a
de facto procedure to derive OOD detectors with respect to in-distribution (ID)
data. Despite its common use, the understanding of the role of fine-tuning and
its necessity for OOD detection is largely unexplored. In this paper, we raise
the question: is fine-tuning necessary for OOD detection? We present a study
investigating the efficacy of directly leveraging pre-trained language models
for OOD detection, without any model fine-tuning on the ID data. We compare the
approach with several competitive fine-tuning objectives, and offer new
insights under various types of distributional shifts. Extensive evaluations on
8 diverse ID-OOD dataset pairs demonstrate near-perfect OOD detection
performance (with 0% FPR95 in many cases), strongly outperforming its
fine-tuned counterparts. We show that using distance-based detection methods,
pre-trained language models are near-perfect OOD detectors when the
distribution shift involves a domain change. Furthermore, we study the effect
of fine-tuning on OOD detection and identify how to balance ID accuracy with
OOD detection performance. Our code is publically available at
https://github.com/Uppaal/lm-ood.
