Web: http://arxiv.org/abs/2205.02014

May 5, 2022, 1:11 a.m. | Bill Yuchen Lin, Sida Wang, Xi Victoria Lin, Robin Jia, Lin Xiao, Xiang Ren, Wen-tau Yih

cs.CL updates on arXiv.org arxiv.org

Real-world natural language processing (NLP) models need to be continually
updated to fix the prediction errors in out-of-distribution (OOD) data streams
while overcoming catastrophic forgetting. However, existing continual learning
(CL) problem setups cannot cover such a realistic and complex scenario. In
response to this, we propose a new CL problem formulation dubbed continual
model refinement (CMR). Compared to prior CL settings, CMR is more practical
and introduces unique challenges (boundary-agnostic and non-stationary
distribution shift, diverse mixtures of multiple OOD data …

arxiv continual data distribution model on

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