Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives

The emergence of generative artificial intelligence (AI) and large language models (LLMs) has marked a new era of natural language processing (NLP), introducing unprecedented capabilities that are revolutionizing various domains. This article explores the current state of these cutting-edge technologies, demonstrating their remarkable advancements and wide-ranging applications. Our article contributes to providing a holistic perspective on the technical foundations, practical applications, and emerging challenges within the evolving landscape of generative AI and LLMs. We believe that understanding the generative capabilities of AI systems and the specific context of LLMs is crucial for researchers, practitioners, and policymakers to collaboratively shape the responsible and ethical integration of these technologies into various domains. Furthermore, we identify and address main research gaps, providing valuable insights to guide future research endeavors within the AI research community.

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Publication Details

Journal
IEEE Transactions on Artificial Intelligence
Published
2024-08-19
DOI
https://doi.org/10.1109/tai.2024.3444742
Citations
95
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
33.00

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article

Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives

Danda B. Rawat, Desta Haileselassie Hagos, Rick Battle
95 citations
IEEE Transactions on Artificial Intelligence
Topic Modeling
33.00
article

Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives

Danda B. Rawat, Desta Haileselassie Hagos, Rick Battle
article en
95 citations

Abstract

The emergence of generative artificial intelligence (AI) and large language models (LLMs) has marked a new era of natural language processing (NLP), introducing unprecedented capabilities that are revolutionizing various domains. This article explores the current state of these cutting-edge technologies, demonstrating their remarkable advancements and wide-ranging applications. Our article contributes to providing a holistic perspective on the technical foundations, practical applications, and emerging challenges within the evolving landscape of generative AI and LLMs. We believe that understanding the generative capabilities of AI systems and the specific context of LLMs is crucial for researchers, practitioners, and policymakers to collaboratively shape the responsible and ethical integration of these technologies into various domains. Furthermore, we identify and address main research gaps, providing valuable insights to guide future research endeavors within the AI research community.

IEEE Transactions on Artificial IntelligenceVol. 5(12)
Howard University (US), Broadcom (United States) (US)
Army Research Laboratory
Quality Education
Openalex Percentile: Top 0%
Topic Modeling
33.00
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