Papers with adaptation time

1 papers
Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation (2022.tacl-1)

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Challenge: Large pretrained language models are often domain- or task-adapted via finetuning or prompting.
Approach: They propose to use domain-adaptive pretraining to prepare large pretrained language models for domain- or task-adaptation by learning to learn the difference between general and adapted PLMs.
Outcome: Experiments on few-shot dialogue completion, low-resource abstractive summarization, and multi-domain language modeling show improvements in adaptation time and performance over finetuning or preparation via domain-adaptive pretraining.

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