22 citations · 33 across the 23 of their papers we have counts for
6 papers · 1 filter
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language
Zekai Qu, Ruobing Xie, Chaojun Xiao +5
With the thriving of pre-trained language model (PLM) widely verified in various of NLP tasks, pioneer efforts attempt to explore the possible cooperation of the general textual in…
Universal Multi-modal Multi-domain Pre-trained Recommendation
Wenqi Sun, Ruobing Xie, Shuqing Bian +2
There is a rapidly-growing research interest in modeling user preferences via pre-training multi-domain interactions for recommender systems. However, Existing pre-trained multi-do…
MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation
Xiaozhi Wang, Hao Peng, Yong Guan +9
Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-eve…
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…
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…