81 citations · 261 across the 39 of their papers we have counts for
9 papers · 2 filters
Boosting Inference Efficiency: Unleashing the Power of Parameter-Shared Pre-trained Language Models
Weize Chen, Xiaoyue Xu, Xu Han +5
Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resource-constrained environments, enabling substantial reductions in model storage and…
Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules
Chaojun Xiao, Yuqi Luo, Wenbin Zhang +8
Pre-trained language models (PLMs) have achieved remarkable results on NLP tasks but at the expense of huge parameter sizes and the consequent computational costs. In this paper, w…
Predicting Emergent Abilities with Infinite Resolution Evaluation
Shengding Hu, Xin Liu, Xu Han +9
The scientific scale-up of large language models (LLMs) necessitates a comprehensive understanding of their scaling properties. However, the existing literature on the scaling prop…
UltraFeedback: Boosting Language Models with Scaled AI Feedback
Ganqu Cui, Lifan Yuan, Ning Ding +9
Learning from human feedback has become a pivot technique in aligning large language models (LLMs) with human preferences. However, acquiring vast and premium human feedback is bot…
Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages
Jinyi Hu, Yuan Yao, Chongyi Wang +13
Recently there has been a significant surge in multimodal learning in terms of both image-to-text and text-to-image generation. However, the success is typically limited to English…
Plug-and-Play Document Modules for Pre-trained Models
Chaojun Xiao, Zhengyan Zhang, Xu Han +7
Large-scale pre-trained models (PTMs) have been widely used in document-oriented NLP tasks, such as question answering. However, the encoding-task coupling requirement results in t…