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Found 7 papers, 5 papers with code
Tell, don't show: Declarative facts influence how LLMs generalize
Nevertheless, the effect of declarative statements on model likelihoods is small in absolute terms and increases surprisingly little with model size (i. e. from 330 million to 175 billion parameters).
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities
Moreover, we show that the confidence loss which is used by Outlier Exposure has an implicit scoring function which differs in a non-trivial fashion from the theoretically optimal scoring function in the case where training and test out-distribution are the same, which again is similar to the one used when training an Energy-Based OOD detector or when adding a background class.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Revisiting Out-of-Distribution Detection: A Simple Baseline is Surprisingly Effective
When trained in a shared fashion with a standard classifier, this binary discriminator reaches an OOD detection performance similar to that of Outlier Exposure.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Provably Robust Detection of Out-of-distribution Data (almost) for free
The application of machine learning in safety-critical systems requires a reliable assessment of uncertainty.
Certifiably Adversarially Robust Detection of Out-of-Distribution Data
Deep neural networks are known to be overconfident when applied to out-of-distribution (OOD) inputs which clearly do not belong to any class.
Adversarial Robustness on In- and Out-Distribution Improves Explainability
Neural networks have led to major improvements in image classification but suffer from being non-robust to adversarial changes, unreliable uncertainty estimates on out-distribution samples and their inscrutable black-box decisions.
Towards neural networks that provably know when they don't know
It has recently been shown that ReLU networks produce arbitrarily over-confident predictions far away from the training data.
