Quantization

method ID: quantization

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AbstractThe deployment of widely used Transformer architecture is challenging because of heavy computation load and memory overhead during inference, especially when the target device is limited in computational resources such …...

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AbstractRecent studies on compression of pretrained language models (e.g., BERT) usually use preserved accuracy as the metric for evaluation. In this paper, we propose two new metrics, label loyalty and …...

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BinaryBERT: Pushing the Limit of BERT Quantization

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AbstractThe rapid development of large pre-trained language models has greatly increased the demand for model compression techniques, among which quantization is a popular solution. In this paper, we propose BinaryBERT, …...

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Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks

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AbstractQuantization, knowledge distillation, and magnitude pruning are among the most popular methods for neural network compression in NLP. Independently, these methods reduce model size and can accelerate inference, but their …...

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A Comparative Study on the Impact of Model Compression Techniques on Fairness in Language Models

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37563