 [2303.12712] Sparks of Artificial General Intelligence: Early experiments with GPT-4






























  











Help spread the word
The arXiv Accessibility Forum takes place this September. Free, fully remote and open to all. Learn more and spread the word.


Learn More





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:2303.12712
  





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:2303.12712 (cs)
    




  [Submitted on 22 Mar 2023 (v1), last revised 13 Apr 2023 (this version, v5)]
Title:Sparks of Artificial General Intelligence: Early experiments with GPT-4
Authors:Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang View a PDF of the paper titled Sparks of Artificial General Intelligence: Early experiments with GPT-4, by S\'ebastien Bubeck and 13 other authors
View PDF

Abstract:Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.
    



Subjects:

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

Cite as:
arXiv:2303.12712 [cs.CL]


 
(or 
arXiv:2303.12712v5 [cs.CL] for this version)
          
 
 

https://doi.org/10.48550/arXiv.2303.12712



Focus to learn more




                arXiv-issued DOI via DataCite
              







Submission history From: Sebastien Bubeck [view email]       [v1]
        Wed, 22 Mar 2023 16:51:28 UTC (13,667 KB)
[v2]
        Fri, 24 Mar 2023 17:07:43 UTC (6,453 KB)
[v3]
        Mon, 27 Mar 2023 22:36:40 UTC (6,470 KB)
[v4]
        Wed, 12 Apr 2023 17:00:10 UTC (12,943 KB)
[v5]
        Thu, 13 Apr 2023 20:41:31 UTC (6,476 KB)



 

Full-text links:
Access Paper:


View a PDF of the paper titled Sparks of Artificial General Intelligence: Early experiments with GPT-4, by S\'ebastien Bubeck and 13 other authorsView PDFTeX SourceOther Formats


view license


 
    Current browse context: cs.CL


< prev

  |   
next >


new
 | 
recent
 | 2023-03

    Change to browse by:
    
cs
cs.AI




References & Citations

NASA ADSGoogle Scholar
Semantic Scholar





 10 blog links (what is this?)
        


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





 





