42 citations · 265 across the 30 of their papers we have counts for
6 papers · 1 filter
LLM Inference Unveiled: Survey and Roofline Model Insights
Zhihang Yuan, Yuzhang Shang, Yang Zhou +11
The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Although the field has expanded and is v…
Uncertainty in Natural Language Processing: Sources, Quantification, and Applications
Mengting Hu, Zhen Zhang, Shiwan Zhao +2
As a main field of artificial intelligence, natural language processing (NLP) has achieved remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in a…
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
Zhen Zhang, Mengting Hu, Shiwan Zhao +6
Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER syste…
RPTQ: Reorder-based Post-training Quantization for Large Language Models
Zhihang Yuan, Lin Niu, Jiawei Liu +7
Large-scale language models (LLMs) have demonstrated impressive performance, but their deployment presents challenges due to their significant memory usage. This issue can be allev…
Federated Nearest Neighbor Machine Translation
Yichao Du, Zhirui Zhang, Bingzhe Wu +3
To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention. Training neural machine translation (NMT) models with traditional F…
A Comprehensive Analysis of Information Leakage in Deep Transfer Learning
Cen Chen, Bingzhe Wu, Minghui Qiu +2
Transfer learning is widely used for transferring knowledge from a source domain to the target domain where the labeled data is scarce. Recently, deep transfer learning has achieve…