most citedICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization

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cs.CL2025

Well Begun is Half Done: Low-resource Preference Alignment by Weak-to-Strong Decoding

Feifan Song, Shaohang Wei, Wen Luo +4

Large Language Models (LLMs) require alignment with human preferences to avoid generating offensive, false, or meaningless content. Recently, low-resource methods for LLM alignment…

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…