This paper introduces a novel, integrated approach AHA ( A daptive H uman-A ssisted OOD learning) to simultaneously address both OOD generalization and detection through a human-assisted framework by labeling data in the wild.

Algorithms, while straightforward in theory, become challenging to deploy in real-world scenarios.

Arya et al. suggest using either supervised or self-supervised learning techniques to train the NN; the former requires access to exact inference

Humans can picture a sound scene given an imprecise natural language description. For example, it is easy to imagine an acoustic environment given a phrase like "the

GPT -4o, on this dataset and find that LLMs are susceptible to adopting incorrect retrieved content, overriding their own correct prior knowledge over 60% of the time. However, the more unrealistic the retrieved content is (i.e. more deviated from