Skip to content
2018
Volume 77, Issue 1
  • ISSN: 0010-096X
  • E-ISSN: 1939-9006

Abstract

This Research Brief discusses transformers—the core engine for most artificial intelligence applications. The brief situates transformer technology within the field of rhetoric and composition by surveying recent studies; highlights the innovative aspects of transformers; and, finally, thinks through (Majdik and Graham) the operations of transformers and generative AI through Miller’s theory of topoi, illustrating one way in which rhetoric and composition scholars and teachers can critically engage with generative AI in instruction and research.

Loading

Article metrics loading...

/content/journals/10.58680/ccc2025771197
2025-09-01
2026-06-16
Loading full text...

Full text loading...

References

  1. Aguilar Gabriel Lorenzo. “Rhetorically Training Students to Generate with AI: Social Justice Applications for AI as Audience.” Computers and Composition, vol. 71, Mar 2024, 102828, https://doi.org/10.1016/j.compcom.2024.102828.
    [Google Scholar]
  2. Alammar Jay. “The Illustrated GPT-2 (Visualizing Transformer Language Models).” https://jalammar.github.io/illustrated-gpt2/.
  3. Alammar Jay. “The Illustrated Transformer,” https://jalammar.github.io/illustrated-transformer/.
  4. Alammar J., and Grootendorst M.Hands-On Large Language Models: Language Understanding and Generation. O’Reilly Media 2024.
    [Google Scholar]
  5. Ardi Muhammad. “Meet GPT, the Decoder-Only Transformer.” Towards Data Science, 6Jan 2025, https://towardsdatascience.com/meet-gpt-the-decoder-only-transformer-12f4a7918b36/.
    [Google Scholar]
  6. Bender E. M., et al. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?.” Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, ACM 2021, pp 610–23. https://doi.org/10.1145/3442188.3445922.
    [Google Scholar]
  7. Bahdanau D., et al. “Neural Machine Translation by Jointly Learning to Align and Translate. arXiv:1409.0473.” arXiv.org, 19May 2016, https://doi.org/10.48550/arXiv.1409.0473.
    [Google Scholar]
  8. Cho A., et al. “Transformer Explainer: Interactive Learning of Text-Generative Models. arXiv:2408.04619.” arXiv.org, https://doi.org/10.48550/arXiv.2408.04619.
    [Google Scholar]
  9. Crawford Kate. The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale U P 2021 https://doi.org/10.2307/j.ctv1ghv45t.
    [Google Scholar]
  10. Cristina Stefania. “What Is Attention?” MachineLearningMastery.Com, 24Aug 2022, https://www.machinelearning-mastery.com/what-is-attention/.
    [Google Scholar]
  11. Hallsby Atilla. “A Copious Void: Rhetoric as Artificial Intelligence 1.0.” Rhetoric Society Quarterly, vol. 54, no. 3 2024, 232–46.
    [Google Scholar]
  12. Karpathy Andrej. “Stanford CS25: V2 I Introduction to Transformers w/Andrej Karpathy” 2023YouTube, https://www.youtube.com/watch?v=XfpMkf4rD6E.
    [Google Scholar]
  13. Knowles Alan M. “Machine-in-the-Loop Writing: Optimizing the Rhetorical Load.” Computers and Composition, vol. 71, Mar 2024, 102826. https://doi.org/10.1016/j.compcom.2024.102826.
    [Google Scholar]
  14. Kulchar Douglas. “Vector Rhetoric: GPT’s Rhetorical Agency.” Philosophy & Rhetoric, vol. 57, no. 2, Sept 2024, pp 194–217. https://doi.org/10.5325/philrhet.57.2.0194.
    [Google Scholar]
  15. LeCun Y., et al. “Deep Learning.” Nature, vol. 521, no. 7553 2015, pp 436–44. https://doi.org/10.1038/nature14539.
    [Google Scholar]
  16. Lin T., et al. “A Survey of Transformers. arXiv:2106.04554.” arXiv.org, 15June 2021, https://doi.org/10.48550/arXiv.2106.04554.
    [Google Scholar]
  17. Luhmann Niklas. “What Is Communication?” Communication Theory, vol. 2, no. 3 1992, pp 251–59. doi:10.1111/j.1468‑2885.1992.tb00042.x.
    [Google Scholar]
  18. Majdik Zoltan P. “A Computational Approach to Assessing Rhetorical Effectiveness: Agentic Framing of Climate Change in the Congressional Record, 1994-2016.” Technical Communication Quarterly, vol. 28, no. 3 2019, pp 207–22, doi:10.1080/10572252.2019.1601774.
    [Google Scholar]
  19. Majdik Zoltan P., and Graham S. Scott. “Rhetoric of/with AI: An Introduction.” Rhetoric Society Quarterly, vol. 54, no. 3, May 2024, pp 222–31. https://doi.org/10.1080/02773945.2024.2343264.
    [Google Scholar]
  20. Majdik Zoltan P., et al. “Sample Size Considerations for Fine-Tuning Large Language Models for Named Entity Recognition Tasks: Methodological Study.” JMIR AI, vol. 3, no. 1, May 2024, e52095. https://doi.org/10.2196/52095.
    [Google Scholar]
  21. Manierre Matt, et al. “Coexisting with ChatGPT: Evaluating a Tool for AI-Based Paper Revision.” Computers and Composition, vol. 76, June 2025, 102923. ScienceDirect, https://doi.org/10.1016/j.compcom.2025.102923.
    [Google Scholar]
  22. Mehlenbacher B., et al. “Synthetic Genres: Expert Genres, Non-Specialist Audiences, and Misinformation in the Artificial Intelligence Age.” Journal of Technical Writing and Communication, vol. 55, no. 2 2025, pp 163–89.
    [Google Scholar]
  23. Miller Carolyn R. “The Aristotelian Topos: Hunting for Novelty.” Foundations for Sociorhetorical Exploration: A Rhetoric of Religious Antiquity Reader vol. 4 2016 pp 95–117.
    [Google Scholar]
  24. Miller Carolyn R.. “What Can Automation Tell Us about Agency?” Rhetoric Society Quarterly, vol. 37, no. 2 2007, pp 137–57. JSTOR, http://www.jstor.org/stable/40232521.
    [Google Scholar]
  25. Ng Jenna. “An Alternative Rationalisation of Creative AI by De-Familiarising Creativity: Towards an Intelligibility of Its Own Terms.” AI for Everyone? Critical Perspectives, edited byVerdegem Pieter, U of Westminster P 2021, pp 49–66. https://doi.org/10.16997/book55.d.
    [Google Scholar]
  26. Omizo Ryan, and Hart-Davidson William. “Is Genre Enough? A Theory of Genre Signaling as Generative AI Rhetoric.” Rhetoric Society Quarterly, vol. 54, no. 3, May 2024, pp 272–85, https://doi.org/10.1080/02773945.2024.2343615.
    [Google Scholar]
  27. Omizo Ryan, et al. “Detecting High-Quality Comments in Written Feedback with a Zero Shot Classifier.” Proceedings of the 39th ACM International Conference on Design of Communication 2021, https://dl.acm.org/doi/10.1145/3472714.3473659.
    [Google Scholar]
  28. OpenAIet al. “GPT-4 Technical Report (No. arXiv:2303.08774).” arXiv 2024, https://doi.org/10.48550/arXiv.2303.08774.
    [Google Scholar]
  29. Ranade Nupoor, and Eyman Douglas. “Introduction: Composing with Generative AI.” Computers and Composition, vol. 71, Mar 2024, 102834. https://doi.org/10.1016/j.compcom.2024.102834.
    [Google Scholar]
  30. Selber Stuart A.Multiliteracies for a Digital Age. Southern Illinois UP 2004.
    [Google Scholar]
  31. Sperber Lisaet al. “Peer and AI Review + Reflection (PAIRR): A Human-Centered Approach to Formative Assessment.” Computers and Composition, vol. 76, June 2025, 102921. https://doi.org/10.1016/j.compcom.2025.102921.
    [Google Scholar]
  32. Swales J. M.Genre Analysis. Cambridge UP 1990.
    [Google Scholar]
  33. Thominet Luke, et al. “How Our AI-Assisted Qualitative Analysis Failed.” Proceedings of the 42nd ACM International Conference on Design of Communication, Association for Computing Machinery 2024, pp 212–16. https://doi.org/10.1145/3641237.3691672.
    [Google Scholar]
  34. Uszkoreit Jakob. “Transformer: A Novel Neural Network Architecture for Language Understanding.” Google Research, 31Apr 2017, https://research.google/blog/transformer-a-novel-neural-network-architecture-for-language-understanding/.
    [Google Scholar]
  35. Varanasi Lakshmi. “A Complete Guide to Nvidia: The Chipmaker Behind the AI Boom.” Business Insider, 2Mar 2025, https://web.archive.org/web/20250302182339/https://www.businessinsider.com/nvidia.
    [Google Scholar]
  36. Vaswani A., et al. “Attention Is All You Need.” Proceedings of the 31st International Conference on Neural Information Processing Systems 2017, pp 6000–10.
    [Google Scholar]
  37. Vetter Matthew A., et al. “Towards a Framework for Local Interrogation of AI Ethics: A Case Study on Text Generators, Academic Integrity, and Composing with ChatGPT.” Computers and Composition, vol. 71, March 2024, 102831. https://doi.org/10.1016/j.compcom.2024.102831.
    [Google Scholar]
  38. Vig Jesse. “A Multiscale Visualization of Attention in the Transformer Model.” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Association for Computational Linguistics 2019, pp 37–42. https://doi.org/10.18653/v1/P19-3007.
    [Google Scholar]
  39. Vig Jesse. “jessevig/bertviz.” GitHub, 17Jan 2025, https://github.com/jessevig/bertviz.
    [Google Scholar]
  40. von Eschenbach Warren J. “Transparency and the Black Box Problem: Why We Do Not Trust AI.” Philosophy & Technology, vol. 34, no. 4, Dec 2021, pp 1607–22. Springer Link, https://doi.org/10.1007/s13347-021-00477-0.
    [Google Scholar]
  41. Wang Zhaozhe. “Post-Rhetoric: A Rhetorical Profile of the Generative Artificial Intelligence Chatbot.” Rhetoric Review, vol. 43, no. 3, July 2024, pp 155–72. https://doi.org/10.1080/07350198.2024.2351723.
    [Google Scholar]
  42. Yang Misti H., and Majdik Zoltan P.. “Pathological Liars: Algorithmic Knowing in the Rhetorical Ecosystem of Wallstreetbets.” Rhetoric Society Quarterly, vol. 54, no. 3, May 2024, pp 286–301. https://doi.org/10.1080/02773945.2024.2343616.
    [Google Scholar]
/content/journals/10.58680/ccc2025771197
Loading
/content/journals/10.58680/ccc2025771197
Loading

Data & Media loading...

  • Article Type: Research Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test