 [2407.14962] Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives




























  








Skip to main content





Grab your spot at the free arXiv Accessibility Forum
Forum Schedule

We gratefully acknowledge support fromthe Simons Foundation, Stockholm University, and all contributors. Donate





 > cs > arXiv:2407.14962
  





Help | Advanced Search




All fields
Title
Author
Abstract
Comments
Journal reference
ACM classification
MSC classification
Report number
arXiv identifier
DOI
ORCID
arXiv author ID
Help pages
Full text




Search















open search






GO



open navigation menu


quick links

Login
Help Pages
About












Computer Science > Computation and Language


arXiv:2407.14962 (cs)
    




  [Submitted on 20 Jul 2024 (v1), last revised 23 Jul 2024 (this version, v2)]
Title:Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives
Authors:Desta Haileselassie Hagos, Rick Battle, Danda B. Rawat View a PDF of the paper titled Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives, by Desta Haileselassie Hagos and 2 other authors
View PDF

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 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.
    


 
Comments:
This version is accepted for publication in the journal of IEEE Transactions on Artificial Intelligence (TAI)


Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as:
arXiv:2407.14962 [cs.CL]


 
(or 
arXiv:2407.14962v2 [cs.CL] for this version)
          
 
 

https://doi.org/10.48550/arXiv.2407.14962



Focus to learn more




                arXiv-issued DOI via DataCite
              







Submission history From: Desta Haileselassie Hagos [view email]       [v1]
        Sat, 20 Jul 2024 18:48:35 UTC (2,640 KB)
[v2]
        Tue, 23 Jul 2024 18:07:28 UTC (2,639 KB)



 

Full-text links:
Access Paper:


View a PDF of the paper titled Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives, by Desta Haileselassie Hagos and 2 other authorsView PDFTeX SourceOther Formats


view license


 
    Current browse context: cs.CL


< prev

  |   
next >


new
 | 
recent
 | 2024-07

    Change to browse by:
    
cs
cs.AI
cs.LG




References & Citations

NASA ADSGoogle Scholar
Semantic Scholar




a
export BibTeX citation
Loading...




BibTeX formatted citation
×


loading...


Data provided by: 




Bookmark





 




Bibliographic Tools

Bibliographic and Citation Tools






Bibliographic Explorer Toggle



Bibliographic Explorer (What is the Explorer?)







Litmaps Toggle



Litmaps (What is Litmaps?)







scite.ai Toggle



scite Smart Citations (What are Smart Citations?)








Code, Data, Media

Code, Data and Media Associated with this Article






Links to Code Toggle



CatalyzeX Code Finder for Papers (What is CatalyzeX?)







DagsHub Toggle



DagsHub (What is DagsHub?)







GotitPub Toggle



Gotit.pub (What is GotitPub?)







Links to Code Toggle



Papers with Code (What is Papers with Code?)







ScienceCast Toggle



ScienceCast (What is ScienceCast?)











Demos

Demos






Replicate Toggle



Replicate (What is Replicate?)







Spaces Toggle



Hugging Face Spaces (What is Spaces?)







Spaces Toggle



TXYZ.AI (What is TXYZ.AI?)








Related Papers

Recommenders and Search Tools






Link to Influence Flower



Influence Flower (What are Influence Flowers?)







Connected Papers Toggle



Connected Papers (What is Connected Papers?)







Core recommender toggle



CORE Recommender (What is CORE?)





Author
Venue
Institution
Topic














        About arXivLabs
      



arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.










Which authors of this paper are endorsers? |
    Disable MathJax (What is MathJax?)
    












About
Help





contact arXivClick here to contact arXiv
 Contact


subscribe to arXiv mailingsClick here to subscribe
 Subscribe











Copyright
Privacy Policy




Web Accessibility Assistance


arXiv Operational Status 
                    Get status notifications via
                    email
                    or slack





 





