March 13, 2024, 4:42 a.m. | Mark D. McDonnell, Dong Gong, Ehsan Abbasnejad, Anton van den Hengel

cs.LG updates on

arXiv:2403.07356v1 Announce Type: cross
Abstract: Continual learning requires a model to adapt to ongoing changes in the data distribution, and often to the set of tasks to be performed. It is rare, however, that the data and task changes are completely unpredictable. Given a description of an overarching goal or data theme, which we call a realm, humans can often guess what concepts are associated with it. We show here that the combination of a large language model and an …

arxiv continual cs.lg data future generative generative models type

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