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Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector
We revisit the likelihood ratio between a pretrained large language model (LLM) and its finetuned variant as a criterion for out-of-distribution (OOD) detection.
Constructing Semantics-Aware Adversarial Examples with Probabilistic Perspective
We propose a probabilistic perspective on adversarial examples.
Falsehoods that ML researchers believe about OOD detection
An intuitive way to detect out-of-distribution (OOD) data is via the density function of a fitted probabilistic generative model: points with low density may be classed as OOD.
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