How does the relative Mahalanobis distance (RMD) method compare to ODIN and other post-hoc OOD detectors on LL

Abstract

Abstract: Out-of-distribution (OOD) detection is a rapidly growing field due to new robustness and security requirements driven by an increased number of AI-based systems. Existing OOD textual detectors often rely on an anomaly score (e.g., Mahalanobis distance) computed on the embedding output of the last layer of the encoder. In this work, we observe that OOD detection performance varies greatly depending on the task and layer output. More importantly, we show that the usual choice (the last layer) is rarely the best one for OOD detection and that far better results could be achieved if the best layer

Research Question

How does the relative Mahalanobis distance (RMD) method compare to ODIN and other post-hoc OOD detectors on LLM embedding spaces for near-distribution outlier detection in large-scale text classification benchmarks like GLUE or SuperGLUE?

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