13 citations · 41 across the 13 of their papers we have counts for
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cs.CL2024★ 1 cited
Towards a Unified View of Preference Learning for Large Language Models: A Survey
Bofei Gao, Feifan Song, Yibo Miao +22
Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial factors to achieve success is aligning the LLM's output with human preferences. This align…
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…