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Found 4 papers, 1 papers with code
Energy and Carbon Considerations of Fine-Tuning BERT
Despite the popularity of the `pre-train then fine-tune' paradigm in the NLP community, existing work quantifying energy costs and associated carbon emissions has largely focused on language model pre-training.
To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing
NLP is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception.
The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment
In this work, we examine this phenomenon through a series of case studies analyzing the effects of model design decisions, framework paradigms, and hardware platforms on total model latency.
Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models
Model compression by way of parameter pruning, quantization, or distillation has recently gained popularity as an approach for reducing the computational requirements of modern deep neural network models for NLP.
