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Model Uncertainty-Aware Knowledge Amalgamation for Pre-Trained Language Models
[article]
2021
arXiv
pre-print
As many fine-tuned pre-trained language models~(PLMs) with promising performance are generously released, investigating better ways to reuse these models is vital as it can greatly reduce the retraining computational cost and the potential environmental side-effects. In this paper, we explore a novel model reuse paradigm, Knowledge Amalgamation~(KA) for PLMs. Without human annotations available, KA aims to merge the knowledge from different teacher-PLMs, each of which specializes in a different
arXiv:2112.07327v1
fatcat:e2metiycsrbrdlqitfzth2ekqe