LLMs Without Deep Neural Networks: New Architecture, Benefits and Case Study
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Computer Science > Machine Learning
Title:LLMs Without Deep Neural Networks: New Architecture, Benefits and Case Study
Abstract:The purpose of this article is to provide validation to my deep neural network alternative in the context of LLMs. Very recently, there has been a significant interest by Chinese researchers in a model called RBF network, as a substitute to standard DNNs, with increased explainability and higher accuracy. It turns out that my new model, discovered independently, is based on the exact same machinery. But with a major twist: it does not need DNN as it finds the global optimum of the loss function in closed form, in one iteration, thus eliminating the tedious training step. Here I provide a high-level overview of my technology, with case study and comparison to similar methods.
| Comments: | 9 pages, 5 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.30385 [cs.LG] |
| (or arXiv:2605.30385v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.30385
arXiv-issued DOI via DataCite
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