8 citations · 22 across the 17 of their papers we have counts for
8 papers · 1 filter
Weighted-Reward Preference Optimization for Implicit Model Fusion
Ziyi Yang, Fanqi Wan, Longguang Zhong +2
While fusing heterogeneous open-source LLMs with varying architectures and sizes can potentially integrate the strengths of different models, existing fusion methods face significa…
FuseChat: Knowledge Fusion of Chat Models
Fanqi Wan, Longguang Zhong, Ziyi Yang +2
While training large language models (LLMs) from scratch can indeed lead to models with distinct capabilities and strengths, it incurs substantial costs and may lead to redundancy…
ProFuser: Progressive Fusion of Large Language Models
Tianyuan Shi, Fanqi Wan, Canbin Huang +6
While fusing the capacities and advantages of various large language models offers a pathway to construct more powerful and versatile models, a fundamental challenge is to properly…
Self-Evolution Fine-Tuning for Policy Optimization
Ruijun Chen, Jiehao Liang, Shiping Gao +2
The alignment of large language models (LLMs) is crucial not only for unlocking their potential in specific tasks but also for ensuring that responses meet human expectations and a…
BlockPruner: Fine-grained Pruning for Large Language Models
Longguang Zhong, Fanqi Wan, Ruijun Chen +2
With the rapid growth in the size and complexity of large language models (LLMs), the costs associated with their training and inference have escalated significantly. Research indi…
Knowledge Fusion of Chat LLMs: A Preliminary Technical Report
Fanqi Wan, Ziyi Yang, Longguang Zhong +3
Recently, FuseLLM introduced the concept of knowledge fusion to transfer the collective knowledge of multiple structurally varied LLMs into a target LLM through lightweight continu…
Knowledge Fusion of Large Language Models
Fanqi Wan, Xinting Huang, Deng Cai +3
While training large language models (LLMs) from scratch can generate models with distinct functionalities and strengths, it comes at significant costs and may result in redundant…
Knowledge Verification to Nip Hallucination in the Bud
Fanqi Wan, Xinting Huang, Leyang Cui +3
While large language models (LLMs) have demonstrated exceptional performance across various tasks following human alignment, they may still generate responses that sound plausible…