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

Danda B. Rawat; Desta Haileselassie Hagos; Rick Battle

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

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arxiv 2407.14962 v5 pith:M26KP6Y7 submitted 2024-07-20 cs.CL cs.AIcs.LG

Desta Haileselassie Hagos , Rick Battle , Danda B. Rawat This is my paper · ORCID

classification cs.CLcs.AIcs.LG

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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 paper explores the current state of these cutting-edge technologies, demonstrating their remarkable advancements and wide-ranging applications. Our paper 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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A Comprehensive Survey on Integrating Large Language Models with Knowledge-Based Methods
cs.CL 2025-01 conditional novelty 3.0 of 10

A narrative review of LLM knowledge integration that categorizes techniques and compiles benchmarks, but lacks a systematic method and contains unreliable citations.

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