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cs.CL2025
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples
Chengqian Gao, Haonan Li, Liu Liu +3
The alignment of large language models (LLMs) often assumes that using more clean data yields better outcomes, overlooking the match between model capacity and example difficulty.…
cs.CL2024
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping
Haoyu Wang, Guozheng Ma, Ziqiao Meng +9
Self-alignment is an effective way to reduce the cost of human annotation while ensuring promising model capability. However, most current methods complete the data collection and…