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arXiv · 2308.14149

Examining User-Friendly and Open-Sourced Large GPT Models: A Survey on Language, Multimodal, and Scientific GPT Models

Kaiyuan Gao·Sunan He·Zhenyu He·Jiacheng Lin·QiZhi Pei·Jie Shao·Wei Zhang
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Abstract

Generative pre-trained transformer (GPT) models have revolutionized the field of natural language processing (NLP) with remarkable performance in various tasks and also extend their power to multimodal domains. Despite their success, large GPT models like GPT-4 face inherent limitations such as considerable size, high computational requirements, complex deployment processes, and closed development loops. These constraints restrict their widespread adoption and raise concerns regarding their responsible development and usage. The need for user-friendly, relatively small, and open-sourced alternative GPT models arises from the desire to overcome these limitations while retaining high performance. In this survey paper, we provide an examination of alternative open-sourced models of large GPTs, focusing on user-friendly and relatively small models that facilitate easier deployment and accessibility. Through this extensive survey, we aim to equip researchers, practitioners, and enthusiasts with a thorough understanding of user-friendly and relatively small open-sourced models of large GPTs, their current state, challenges, and future research directions, inspiring the development of more efficient, accessible, and versatile GPT models that cater to the broader scientific community and advance the field of general artificial intelligence. The source contents are continuously updating in https://github.com/GPT-Alternatives/gpt_alternatives.

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Kaiyuan Gao, Sunan He, Zhenyu He, Jiacheng Lin, QiZhi Pei, Jie Shao, Wei Zhang. 2023-08-27. Examining User-Friendly and Open-Sourced Large GPT Models: A Survey on Language, Multimodal, and Scientific GPT Models. https://arxiv.org/abs/2308.14149

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