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The Trade-off between Performance, Efficiency, and Fairness in Adapter Modules for Text Classification
Current natural language processing (NLP) research tends to focus on only one or, less frequently, two dimensions - e. g., performance, privacy, fairness, or efficiency - at a time, which may lead to suboptimal conclusions and often overlooking the broader goal of achieving trustworthy NLP.
Knowledge Distillation vs. Pretraining from Scratch under a Fixed (Computation) Budget
We further find that KD yields larger gains over pretraining from scratch when the data must be repeated under the fixed computation budget.
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