Transformer-based pretrained language models (T-PTLMs) have achieved great success in
almost every NLP task. The evolution of these models started with GPT and BERT. These …

Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

How to efficiently serve ever-larger trained natural language models in practice has become
exceptionally challenging even for powerful cloud servers due to their prohibitive …

Several post-training quantization methods have been applied to large language models
(LLMs), and have been shown to perform well down to 8-bits. We find that these methods …

Recently, transformer has achieved remarkable performance on a variety of computer vision
applications. Compared with mainstream convolutional neural networks, vision transformers …

Transformer-based models have pushed state of the art in many areas of NLP, but our
understanding of what is behind their success is still limited. This paper is the first survey of …

Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results
in many Natural Language Processing tasks. However, their memory footprint, inference …

Generative Large Language Models (LLMs) have demonstrated remarkable results for a
wide range of tasks. However, deploying these models for inference has been a significant …

Pruning is an effective way to reduce the huge inference cost of Transformer models.
However, prior work on pruning Transformers requires retraining the models. This can add …

Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …