Understanding the Conceptual Structure of Large Language Models through Bibliographical Network-Reference-Cited by-同舟云学术

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Understanding the Conceptual Structure of Large Language Models through Bibliographical Network

Published:2024-05-23 Issue: Volume:1 Page:1-7
ISSN:
Container-title:2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)
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Author:

Duarte-Martínez V.¹,Perez I.J.²,Ducange P.³,Cobo M.J²

Affiliation:

1. ESPOL Polytechnic University,Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación,Guayaquil,Ecuador,Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863

2. University of Granada,Dpt. of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI),Spain

3. University of Pisa,Dpt. of Information Engineering,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10569136/10569095/10569112.pdf?arnumber=10569112

Reference19 articles.

1. Improving language understanding by generative pre-training;Radford,2018

2. Bert: Pre-training of deep bidirectional transformers for language understanding;Devlin,2019

3. Exploring the limits of transfer learning with a unified text-to-text transformer;Raffel;Journal of Machine Learning Research,2020

4. Language models are few-shot learners;Brown;Advances in Neural Information Processing Systems,2020

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