2 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.CL2024★ 1 cited
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization
Feifan Song, Yuxuan Fan, Xin Zhang +2
Large Language Models (LLMs) rely on Human Preference Alignment (HPA) to ensure the generation of safe content. Due to the heavy cost associated with fine-tuning, fine-tuning-free…
cs.CL2023★ 2 cited
Can We Edit Factual Knowledge by In-Context Learning?
Ce Zheng, Lei Li, Qingxiu Dong +4
Previous studies have shown that large language models (LLMs) like GPTs store massive factual knowledge in their parameters. However, the stored knowledge could be false or out-dat…