6 citations · 13 across the 19 of their papers we have counts for
18 papers · 1 filter
Schema as Parameterized Tools for Universal Information Extraction
Sheng Liang, Yongyue Zhang, Yaxiong Wu +2
Universal information extraction (UIE) primarily employs an extractive generation approach with large language models (LLMs), typically outputting structured information based on p…
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
Yehui Tang, Xiaosong Li, Fangcheng Liu +19
The surgence of Mixture of Experts (MoE) in Large Language Models promises a small price of execution cost for a much larger model parameter count and learning capacity, because on…
Pangu Light: Weight Re-Initialization for Pruning and Accelerating LLMs
Hanting Chen, Jiarui Qin, Jialong Guo +15
Large Language Models (LLMs) deliver state-of-the-art capabilities across numerous tasks, but their immense size and inference costs pose significant computational challenges for p…
Instruction-Tuning Data Synthesis from Scratch via Web Reconstruction
Yuxin Jiang, Yufei Wang, Chuhan Wu +8
The improvement of LLMs' instruction-following capabilities depends critically on the availability of high-quality instruction-response pairs. While existing automatic data synthet…
ToolACE-DEV: Self-Improving Tool Learning via Decomposition and EVolution
Xu Huang, Weiwen Liu, Xingshan Zeng +8
The tool-using capability of large language models (LLMs) enables them to access up-to-date external information and handle complex tasks. Current approaches to enhancing this capa…
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
Yehui Tang, Yichun Yin, Yaoyuan Wang +71
Sparse large language models (LLMs) with Mixture of Experts (MoE) and close to a trillion parameters are dominating the realm of most capable language models. However, the massive…