8 citations · 14 across the 5 of their papers we have counts for
9 papers
Mutual-Taught for Co-adapting Policy and Reward Models
Tianyuan Shi, Canbin Huang, Fanqi Wan +5
During the preference optimization of large language models (LLMs), distribution shifts may arise between newly generated model samples and the data used to train the reward model…
FuseRL: Dense Preference Optimization for Heterogeneous Model Fusion
Longguang Zhong, Fanqi Wan, Ziyi Yang +3
Heterogeneous model fusion enhances the performance of LLMs by integrating the knowledge and capabilities of multiple structurally diverse models. However, existing approaches ofte…
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…
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…
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…
PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection
Tao Yang, Tianyuan Shi, Fanqi Wan +4
Recent advances in large language models (LLMs), such as ChatGPT, have showcased remarkable zero-shot performance across various NLP tasks. However, the potential of LLMs in person…