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When deploying machine learning systems to the wild, it is highly desirablefor them to effectively leverage prior knowledge to the unfamiliar domain whilealso firing alarms to anomalous inputs. In order to address these requirements,Universal Domain Adaptation (UniDA) has emerged as a novel research area incomputer vision, focusing on achieving both adaptation ability and robustness(i.e., the ability to detect out-of-distribution samples). While UniDA has ledsignificant progress in computer vision, its application on language inputstill needs to be explored despite its feasibility. In this paper, we propose acomprehensive benchmark for natural language that offers thorough viewpoints ofthe model's generalizability and robustness. Our benchmark encompasses multipledatasets with varying difficulty levels and characteristics, including temporalshifts and diverse domains. On top of our testbed, we validate existing UniDAmethods from computer vision and state-of-the-art domain adaptation techniquesfrom NLP literature, yielding valuable findings: We observe that UniDA methodsoriginally designed for image input can be effectively transferred to thenatural language domain while also underscoring the effect of adaptationdifficulty in determining the model's performance.

Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP