6 citations · 56 across the 37 of their papers we have counts for
6 papers · 1 filter
Sparse Low-rank Adaptation of Pre-trained Language Models
Ning Ding, Xingtai Lv, Qiaosen Wang +4
Fine-tuning pre-trained large language models in a parameter-efficient manner is widely studied for its effectiveness and efficiency. The popular method of low-rank adaptation (LoR…
Large Language Models are Zero Shot Hypothesis Proposers
Biqing Qi, Kaiyan Zhang, Haoxiang Li +4
Significant scientific discoveries have driven the progress of human civilisation. The explosion of scientific literature and data has created information barriers across disciplin…
CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model
Kaiyan Zhang, Ning Ding, Biqing Qi +3
Instruction tuning has recently been recognized as an effective way of aligning Large Language Models (LLMs) to enhance their generalization ability across various tasks. However,…
Empowering Private Tutoring by Chaining Large Language Models
Yulin Chen, Ning Ding, Hai-Tao Zheng +3
Artificial intelligence has been applied in various aspects of online education to facilitate teaching and learning. However, few approaches has been made toward a complete AI-powe…
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
Ning Ding, Yulin Chen, Bokai Xu +6
Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT. Scaling the diversity and quality of such dat…
PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning
Xuekai Zhu, Biqing Qi, Kaiyan Zhang +3
While large language models (LLMs) excel in various natural language processing tasks, their huge size and the inaccessibility of parameters present challenges for practical deploy…