Attention is All Large Language Model Need

With the advent of the Transformer, the attention mechanism has been applied to Large Language Model (LLM), evolving from initial single- modal large models to today's multi-modal large models. This has greatly propelled the development of Artificial Intelligence (AI) and ushered humans into the era of large models. Single-modal large models can be broadly categorized into three types based on their application domains: Text LLM for Natural Language Processing (NLP), Image LLM for Computer Vision (CV), and Audio LLM for speech interaction. Multi-modal large models, on the other hand, can leverage multiple data sources simultaneously to optimize the model. This article also introduces the training process of the GPT series. Large models have also had a significant impact on industry and society, bringing with them a number of unresolved problems. The purpose of this article is to assist researchers in comprehending the various forms of LLM, as well as its development, pre- training architecture, difficulties, and future objectives.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-02-17
DOI
https://doi.org/10.1051/itmconf/20257302025/pdf
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Attention is All Large Language Model Need

Yuxin Liu
Springer Link (Chiba Institute of Technology)
Multimodal Machine Learning Applications
article

Attention is All Large Language Model Need

Yuxin Liu
article en

Abstract

With the advent of the Transformer, the attention mechanism has been applied to Large Language Model (LLM), evolving from initial single- modal large models to today's multi-modal large models. This has greatly propelled the development of Artificial Intelligence (AI) and ushered humans into the era of large models. Single-modal large models can be broadly categorized into three types based on their application domains: Text LLM for Natural Language Processing (NLP), Image LLM for Computer Vision (CV), and Audio LLM for speech interaction. Multi-modal large models, on the other hand, can leverage multiple data sources simultaneously to optimize the model. This article also introduces the training process of the GPT series. Large models have also had a significant impact on industry and society, bringing with them a number of unresolved problems. The purpose of this article is to assist researchers in comprehending the various forms of LLM, as well as its development, pre- training architecture, difficulties, and future objectives.

Springer Link (Chiba Institute of Technology)
Quality Education
Openalex Percentile: Top 29%
Multimodal Machine Learning Applications
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Attention is All Large Language Model Need — Yuxin Liu · Springer Link (Chiba Institute of Technology) (2025) | TGRS Research Map | TGRS