7 citations · 14 across the 13 of their papers we have counts for
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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
Fine-Tuning Language Models with Reward Learning on Policy
Hao Lang, Fei Huang, Yongbin Li
Reinforcement learning from human feedback (RLHF) has emerged as an effective approach to aligning large language models (LLMs) to human preferences. RLHF contains three steps, i.e…