Open Access

REVIEW

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

by Zheyi Chen^1,#, Liuchang Xu^1,#, Hongting Zheng¹, Luyao Chen¹, Amr Tolba^2,3, Liang Zhao⁴, Keping Yu^5,*, Hailin Feng^1,*

1 College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, 311300, China
2 Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia
3 Mathematics and Computer Science Department, Faculty of Science, Menofia University, Shebin El Kom, Menoufia Governorate, 32511, Egypt
4 School of Computer Science, Shenyang Aerospace University, Shenyang, 110136, China
5 Graduate School of Science and Engineering, Hosei University, Tokyo, 184-8584, Japan

* Corresponding Authors: Keping Yu. Email: ; Hailin Feng. Email:

Computers, Materials & Continua 2024, 80(2), 1753-1808. https://doi.org/10.32604/cmc.2024.052618

Received 09 April 2024; Accepted 09 July 2024; Issue published 15 August 2024

Abstract

Since the 1950s, when the Turing Test was introduced, there has been notable progress in machine language intelligence. Language modeling, crucial for AI development, has evolved from statistical to neural models over the last two decades. Recently, transformer-based Pre-trained Language Models (PLM) have excelled in Natural Language Processing (NLP) tasks by leveraging large-scale training corpora. Increasing the scale of these models enhances performance significantly, introducing abilities like context learning that smaller models lack. The advancement in Large Language Models, exemplified by the development of ChatGPT, has made significant impacts both academically and industrially, capturing widespread societal interest. This survey provides an overview of the development and prospects from Large Language Models (LLM) to Large Multimodal Models (LMM). It first discusses the contributions and technological advancements of LLMs in the field of natural language processing, especially in text generation and language understanding. Then, it turns to the discussion of LMMs, which integrates various data modalities such as text, images, and sound, demonstrating advanced capabilities in understanding and generating cross-modal content, paving new pathways for the adaptability and flexibility of AI systems. Finally, the survey highlights the prospects of LMMs in terms of technological development and application potential, while also pointing out challenges in data integration, cross-modal understanding accuracy, providing a comprehensive perspective on the latest developments in this field.

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Keywords

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