3 citations · 8 across the 12 of their papers we have counts for
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cs.AI2024★ 3 cited
Harnessing Diversity for Important Data Selection in Pretraining Large Language Models
Chi Zhang, Huaping Zhong, Kuan Zhang +10
Data selection is of great significance in pre-training large language models, given the variation in quality within the large-scale available training corpora. To achieve this, re…
cs.CL2024★ 1 cited
Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration
Tianyi Bai, Ling Yang, Zhen Hao Wong +9
Efficient data selection is crucial to accelerate the pretraining of language model (LMs). While various methods have been proposed to enhance data efficiency, limited research has…
cs.CL2024★ 2 cited
LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training
Tong Zhu, Xiaoye Qu, Daize Dong +4
Mixture-of-Experts (MoE) has gained increasing popularity as a promising framework for scaling up large language models (LLMs). However, training MoE from scratch in a large-scale…