61 citations · 133 across the 9 of their papers we have counts for
5 papers · 1 filter
Large Language Models Understand and Can be Enhanced by Emotional Stimuli
Cheng Li, Jindong Wang, Yixuan Zhang +6
Emotional intelligence significantly impacts our daily behaviors and interactions. Although Large Language Models (LLMs) are increasingly viewed as a stride toward artificial gener…
Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases
Xiaoxia Wu, Cheng Li, Reza Yazdani Aminabadi +2
Improving the deployment efficiency of transformer-based language models has been challenging given their high computation and memory cost. While INT8 quantization has recently bee…
Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers
Zhewei Yao, Xiaoxia Wu, Conglong Li +4
Large-scale transformer models have become the de-facto architectures for various machine learning applications, e.g., CV and NLP. However, those large models also introduce prohib…
LAMPRET: Layout-Aware Multimodal PreTraining for Document Understanding
Te-Lin Wu, Cheng Li, Mingyang Zhang +3
Document layout comprises both structural and visual (eg. font-sizes) information that is vital but often ignored by machine learning models. The few existing models which do use l…
Downstream Model Design of Pre-trained Language Model for Relation Extraction Task
Cheng Li, Ye Tian
Supervised relation extraction methods based on deep neural network play an important role in the recent information extraction field. However, at present, their performance still…