Document Open Access Logo

Large Language Models: Compilers for the 4^{th} Generation of Programming Languages? (Short Paper)

Authors Francisco S. Marcondes , José João Almeida , Paulo Novais

PDF
Thumbnail PDF

File

OASIcs.SLATE.2023.10.pdf
  • Filesize: 0.8 MB
  • 8 pages

Document Identifiers

Author Details

Francisco S. Marcondes
  • ALGORITMI Research Centre/LASI, University of Minho, Braga, Portugal
José João Almeida
  • ALGORITMI Research Centre/LASI, University of Minho, Braga, Portugal
Paulo Novais
  • ALGORITMI Research Centre/LASI, University of Minho, Braga, Portugal

Funding

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

Cite As Get BibTex

Francisco S. Marcondes, José João Almeida, and Paulo Novais. Large Language Models: Compilers for the 4^{th} Generation of Programming Languages? (Short Paper). In 12th Symposium on Languages, Applications and Technologies (SLATE 2023). Open Access Series in Informatics (OASIcs), Volume 113, pp. 10:1-10:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/OASIcs.SLATE.2023.10

Abstract

This paper explores the possibility of large language models as a fourth generation programming language compiler. This is based on the idea that large language models are able to translate a natural language specification into a program written in a particular programming language. In other words, just as high-level languages provided an additional language abstraction to assembly code, large language models can provide an additional language abstraction to high-level languages. This interpretation allows large language models to be thought of through the lens of compiler theory, leading to insightful conclusions.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Artificial intelligence
  • Computing methodologies → Natural language processing
  • Software and its engineering → Compilers
Keywords
  • programming language
  • compiler
  • large language model

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Alfred V Aho, Monica S Lam, Ravi Sethi, and Jeffrey D Ullman. Compilers: principles, techniques and tools. Pearson, 2020. Google Scholar
  2. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33, 2020. Google Scholar
  3. Robert B Grady and Deborah L Caswell. Software metrics: establishing a company-wide program. Prentice-Hall, Inc., 1987. Google Scholar
  4. Ivar Jacobson, Ian Spence, and Brian Kerr. Use-case 2.0. Queue, 14(1):94-123, 2016. Google Scholar
  5. Dan Jurafsky and James H. Martin. Speech and Language Processing. draft (https://web.stanford.edu/~jurafsky/slp3/), third edition, 2023. Google Scholar
  6. Donald Ervin Knuth. Literate programming. The computer journal, 27(2):97-111, 1984. Google Scholar
  7. Philippe B Kruchten. The 4+ 1 view model of architecture. IEEE software, 12(6):42-50, 1995. Google Scholar
  8. Chris Raistrick, Paul Francis, John Wright, Colin Carter, and Ian Wilkie. Model driven architecture with executable UML, volume 1. Cambridge University Press, 2004. Google Scholar
  9. Elvis Saravia. Prompt engineering guide, 2023. URL: https://www.promptingguide.ai/.
  10. Robert W Sebesta. Concepts of programming languages. Pearson Education, 2019. Google Scholar
  11. J.F. Smart and J. Molak. BDD in Action, Second Edition: Behavior-Driven Development for the Whole Software Lifecycle. Manning, 2023. Google Scholar
  12. Bernhard Thalheim and Hannu Jaakkola. Model-based fifth generation programming. Information Modelling and Knowledge Bases, 31:381-400, 2020. Google Scholar
  13. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. Google Scholar
  14. Karl Wiegers. More about software requirements. Microsoft Press, 2005. Google Scholar
  15. Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. Multimodal chain-of-thought reasoning in language models. arXiv:2302.00923, 2023. Google Scholar
  16. Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. A survey of large language models. arXiv preprint arXiv:2303.18223, 2023. Google Scholar
  17. Majd Zohri Yafi. A Syntactical Reverse Engineering Approach to Fourth Generation Programming Languages Using Formal Methods. PhD thesis, University of Essex, 2022. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact