5 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.CL2024
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment
Feifan Song, Bowen Yu, Hao Lang +4
Alignment with human preference prevents large language models (LLMs) from generating misleading or toxic content while requiring high-cost human feedback. Assuming resources of hu…
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★ 5 cited
Making Large Language Models Better Reasoners with Alignment
Peiyi Wang, Lei Li, Liang Chen +5
Reasoning is a cognitive process of using evidence to reach a sound conclusion. The reasoning capability is essential for large language models (LLMs) to serve as the brain of the…