10 citations · 12 across the 6 of their papers we have counts for
4 papers · 1 filter
Uncertainty Aware Learning for Language Model Alignment
Yikun Wang, Rui Zheng, Liang Ding +3
As instruction-tuned large language models (LLMs) evolve, aligning pretrained foundation models presents increasing challenges. Existing alignment strategies, which typically lever…
Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning
Xiao Wang, Tianze Chen, Xianjun Yang +3
The open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. This includes both base models, which are pre-trained on…
Navigating the OverKill in Large Language Models
Chenyu Shi, Xiao Wang, Qiming Ge +7
Large language models are meticulously aligned to be both helpful and harmless. However, recent research points to a potential overkill which means models may refuse to answer beni…
Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback
Songyang Gao, Qiming Ge, Wei Shen +9
The success of AI assistants based on Language Models (LLMs) hinges on Reinforcement Learning from Human Feedback (RLHF) to comprehend and align with user intentions. However, trad…