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Is there some research using the large language model rather than transformer architecture?

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Research has indeed explored the utilization of large language models (LLMs) beyond the transformer architecture. While transformers have shown remarkable capabilities in various tasks , recent studies have combined convolutional layers with transformers for LLMs, particularly in the realm of automatic speech recognition. By adapting these architectures in a causal setup, significant performance improvements have been achieved . This innovative approach merges the strengths of transformers in capturing long-range dependencies with the feature extraction abilities of convolutional layers, showcasing a robust speech architecture applicable beyond speech tasks for large-scale language modeling. Such research highlights the continuous evolution and exploration of diverse neural architecture combinations to enhance the efficiency and effectiveness of LLMs in various applications.

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Open access•Posted Content•DOI Conformer LLMs -- Convolution Augmented Large Language Models 08065912405 01 Jul 2023	Not addressed in the paper.
Open access•Posted Content•DOI Variational Monte Carlo with Large Patched Transformers 06 Jun 2023	Not addressed in the paper.
Peer Review Large Language Models Michael R. Douglas 11 Jul 2023	Not addressed in the paper.
Journal Article•DOI Conformer LLMs - Convolution Augmented Large Language Models Prateek Verma 02 Jul 2023-arXiv.org	Not addressed in the paper.
Variational Monte Carlo with Large Patched Transformers Kyle Sprague, Stefanie Czischek - Show less +1 more 06 Jun 2023	Not addressed in the paper.

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