  As language models become more general purpose, increased attention needs to
be paid to detecting out-of-distribution (OOD) instances, i.e., those not
belonging to any of the distributions seen during training. Existing methods
for detecting OOD data are computationally complex and storage-intensive. We
propose a novel soft clustering approach for OOD detection based on
non-negative kernel regression. Our approach greatly reduces computational and
space complexities (up to 11x improvement in inference time and 87% reduction
in storage requirements) and outperforms existing approaches by up to 4 AUROC
points on four different benchmarks. We also introduce an entropy-constrained
version of our algorithm, which leads to further reductions in storage
requirements (up to 97% lower than comparable approaches) while retaining
competitive performance. Our soft clustering approach for OOD detection
highlights its potential for detecting tail-end phenomena in extreme-scale data
settings.
