102 citations · 280 across the 26 of their papers we have counts for
5 papers · 1 filter
Can Small Training Runs Reliably Guide Data Curation? Rethinking Proxy-Model Practice
Jiachen T. Wang, Tong Wu, Kaifeng Lyu +4
Data teams at frontier AI companies routinely train small proxy models to make critical decisions about pretraining data recipes for full-scale training runs. However, the communit…
Shift is Good: Mismatched Data Mixing Improves Test Performance
Marko Medvedev, Kaifeng Lyu, Zhiyuan Li +1
We consider training and testing on mixture distributions with different training and test proportions. We show that in many settings, and in some sense generically, distribution s…
Data Mixing Can Induce Phase Transitions in Knowledge Acquisition
Xinran Gu, Kaifeng Lyu, Jiazheng Li +1
Large Language Models (LLMs) are typically trained on data mixtures: most data come from web scrapes, while a small portion is curated from high-quality sources with dense domain-s…
Towards Understanding Text Hallucination of Diffusion Models via Local Generation Bias
Rui Lu, Runzhe Wang, Kaifeng Lyu +3
Score-based diffusion models have achieved incredible performance in generating realistic images, audio, and video data. While these models produce high-quality samples with impres…
Weak-to-Strong Generalization Even in Random Feature Networks, Provably
Marko Medvedev, Kaifeng Lyu, Dingli Yu +3
Weak-to-Strong Generalization (Burns et al., 2024) is the phenomenon whereby a strong student, say GPT-4, learns a task from a weak teacher, say GPT-2, and ends up significantly ou…