The Numerical Linear Algebra of Large Language Models
Survey maps numerical linear algebra methods used inside large language models for NLA specialists.
This survey paper targets numerical linear algebra practitioners and explains core large language model concepts in terms they can use. It reviews the matrix and tensor methods underlying LLM techniques and highlights recent NLA contributions to the field. The authors frame today's machine-learning wave as the latest scientific demand on numerical methods, and argue matrix methods will grow more important as systems approach artificial general intelligence.
- Survey aimed at numerical linear algebra specialists.
- Explains LLM concepts through matrix and tensor methods.
- Reviews recent NLA contributions to language-model techniques.
- Argues matrix methods will matter more as systems approach AGI.
From the original article
1 min read at huggingface.co
Numerical Linear Algebra (NLA) has consistently played a vital role in advancing science by providing tools to solve fundamental problems encountered in scientific and engineering applications. Over the decades, it has continually evolved to meet the demands driven by successive waves of scientific discovery.
By Abdelkader Baggag, Yousef Saad · huggingface.co
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