34 citations · 69 across the 18 of their papers we have counts for
6 papers · 1 filter
C-MOP: Integrating Momentum and Boundary-Aware Clustering for Enhanced Prompt Evolution
Binwei Yan, Yifei Fu, Mingjian Zhu +4
Automatic prompt optimization is a promising direction to boost the performance of Large Language Models (LLMs). However, existing methods often suffer from noisy and conflicting u…
Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition
Hanting Chen, Yasheng Wang, Kai Han +21
This work presents Pangu Embedded, an efficient Large Language Model (LLM) reasoner developed on Ascend Neural Processing Units (NPUs), featuring flexible fast and slow thinking ca…
Transferable text data distillation by trajectory matching
Rong Yao, Hailin Hu, Yifei Fu +5
In the realm of large language model (LLM), as the size of large models increases, it also brings higher training costs. There is a urgent need to minimize the data size in LLM tra…
Saliency-driven Dynamic Token Pruning for Large Language Models
Yao Tao, Yehui Tang, Yun Wang +3
Despite the recent success of large language models (LLMs), LLMs are particularly challenging in long-sequence inference scenarios due to the quadratic computational complexity of…
PanGu-: Enhancing Language Model Architectures via Nonlinearity Compensation
Yunhe Wang, Hanting Chen, Yehui Tang +17
The recent trend of large language models (LLMs) is to increase the scale of both model size (\aka the number of parameters) and dataset to achieve better generative ability, which…
Data-Free Distillation of Language Model by Text-to-Text Transfer
Zheyuan Bai, Xinduo Liu, Hailin Hu +3
Data-Free Knowledge Distillation (DFKD) plays a vital role in compressing the model when original training data is unavailable. Previous works for DFKD in NLP mainly focus on disti…