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cs.CL2024
Investigating Low-Rank Training in Transformer Language Models: Efficiency and Scaling Analysis
Xiuying Wei, Skander Moalla, Razvan Pascanu +1
State-of-the-art LLMs often rely on scale with high computational costs, which has sparked a research agenda to reduce parameter counts and costs without significantly impacting pe…
cs.CL2024
Building on Efficient Foundations: Effectively Training LLMs with Structured Feedforward Layers
Xiuying Wei, Skander Moalla, Razvan Pascanu +1
State-of-the-art results in large language models (LLMs) often rely on scale, which becomes computationally expensive. This has sparked a research agenda to reduce these models' pa…