37 citations · 89 across the 10 of their papers we have counts for
4 papers · 1 filter
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
Ruyue Liu, Rong Yin, Xiangzhen Bo +5
Federated graph learning (FGL) has gained significant attention for enabling heterogeneous clients to process their private graph data locally while interacting with a centralized…
Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven Distillation
Xunyu Zhu, Jian Li, Can Ma +1
Large Language Models (LLMs) demonstrate exceptional reasoning capabilities, often achieving state-of-the-art performance in various tasks. However, their substantial computational…
Key-Point-Driven Mathematical Reasoning Distillation of Large Language Model
Xunyu Zhu, Jian Li, Can Ma +1
Large Language Models (LLMs) have demonstrated exceptional proficiency in mathematical reasoning tasks due to their extensive parameter counts and training on vast datasets. Despit…
Distilling Mathematical Reasoning Capabilities into Small Language Models
Xunyu Zhu, Jian Li, Yong Liu +2
This work addresses the challenge of democratizing advanced Large Language Models (LLMs) by compressing their mathematical reasoning capabilities into sub-billion parameter Small L…