13 citations · 13 across the 4 of their papers we have counts for
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cs.LG2026
Explaining Data Mixing Scaling Laws
Rui Dai, Shuran Zheng
Recent research has established empirical scaling laws to predict model performance on multi-domain data mixtures. However, a theoretical understanding of these model loss behavior…
cs.LG2025
Leveraging Submodule Linearity Enhances Task Arithmetic Performance in LLMs
Rui Dai, Sile Hu, Xu Shen +3
Task arithmetic is a straightforward yet highly effective strategy for model merging, enabling the resultant model to exhibit multi-task capabilities. Recent research indicates tha…
cs.LG2023★ 13 cited
Moderately Distributional Exploration for Domain Generalization
Rui Dai, Yonggang Zhang, Zhen Fang +2
Domain generalization (DG) aims to tackle the distribution shift between training domains and unknown target domains. Generating new domains is one of the most effective approaches…