Add to ORKGTransformer-based pre-trained language models (PLMs) mostly suffer from excessive overhead despite their advanced capacity. For resource-constrained devices, there is an urgent need for a spatially and temporally efficient model which retains the major capacity of PLMs. However, existing statically compressed models are unaware of the diverse complexities between input instances, potentially resulting in redundancy and inadequacy for simple and complex inputs. Also, miniature models with early exiting encounter challenges in the trade-off between making predictions and serving the deeper layers. Motivated by such considerations, we propose a collaborative optimization for PLMs that integrates static model compression and dynamic inference a...
Real-world business applications require a trade-off between language model performance and size. We...
Prior work shows that it is possible to expand pretrained Masked Language Models (MLMs) to new langu...
Large language models have become a vital component in modern NLP, achieving state of the art perfor...
Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resourc...
As language models increase in size by the day, methods for efficient inference are critical to leve...
LLMs or Large Language Models are the machine learning models that are used to understand and genera...
Recent advances in Transformer-based large language models (LLMs) have led to significant performanc...
Fine-tuning BERT-based models is resource-intensive in memory, computation, and time. While many pri...
Despite achieving state-of-the-art performance on many NLP tasks, the high energy cost and long infe...
Scaling language models with more data, compute and parameters has driven significant progress in na...
Limited computational budgets often prevent transformers from being used in production and from havi...
There are growing interests in adapting large-scale language models using parameter-efficient fine-t...
Many NLP tasks benefit from using large language models (LLMs) that often have more than 100 billion...
Pretrained language models have become the standard approach for many NLP tasks due to strong perfor...
Despite achieving state-of-the-art performance on many NLP tasks, the high energy cost and long infe...
Real-world business applications require a trade-off between language model performance and size. We...
Prior work shows that it is possible to expand pretrained Masked Language Models (MLMs) to new langu...
Large language models have become a vital component in modern NLP, achieving state of the art perfor...
Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resourc...
As language models increase in size by the day, methods for efficient inference are critical to leve...
LLMs or Large Language Models are the machine learning models that are used to understand and genera...
Recent advances in Transformer-based large language models (LLMs) have led to significant performanc...
Fine-tuning BERT-based models is resource-intensive in memory, computation, and time. While many pri...
Despite achieving state-of-the-art performance on many NLP tasks, the high energy cost and long infe...
Scaling language models with more data, compute and parameters has driven significant progress in na...
Limited computational budgets often prevent transformers from being used in production and from havi...
There are growing interests in adapting large-scale language models using parameter-efficient fine-t...
Many NLP tasks benefit from using large language models (LLMs) that often have more than 100 billion...
Pretrained language models have become the standard approach for many NLP tasks due to strong perfor...
Despite achieving state-of-the-art performance on many NLP tasks, the high energy cost and long infe...
Real-world business applications require a trade-off between language model performance and size. We...
Prior work shows that it is possible to expand pretrained Masked Language Models (MLMs) to new langu...
Large language models have become a vital component in modern NLP, achieving state of the art perfor...