# How to Do Things with Deep Learning Code

arXiv: https://arxiv.org/abs/2304.09406 · HTML: https://mlsanity.com/paper/2304.09406/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [The Principles of Deep Learning Theory](https://mlsanity.com/paper/2106.10165/) (2021)
- [The Modern Mathematics of Deep Learning](https://mlsanity.com/paper/2105.04026/) (2021)

## Built on by (most cited first, 1 of 1)

- [Optimization Is Not All You Need](https://mlsanity.com/paper/2607.11977/) (2026)

## Abstract (arXiv)

The premise of this article is that a basic understanding of the composition and functioning of large language models is critically urgent. To that end, we extract a representational map of OpenAI's GPT-2 with what we articulate as two classes of deep learning code, that which pertains to the model and that which underwrites applications built around the model. We then verify this map through case studies of two popular GPT-2 applications: the text adventure game, AI Dungeon, and the language art project, This Word Does Not Exist. Such an exercise allows us to test the potential of Critical Code Studies when the object of study is deep learning code and to demonstrate the validity of code as an analytical focus for researchers in the subfields of Critical Artificial Intelligence and Critical Machine Learning Studies. More broadly, however, our work draws attention to the means by which ordinary users might interact with, and even direct, the behavior of deep learning systems, and by extension works toward demystifying some of the auratic mystery of "AI." What is at stake is the possibility of achieving an informed sociotechnical consensus about the responsible applications of large language models, as well as a more expansive sense of their creative capabilities-indeed, understanding how and where engagement occurs allows all of us to become more active participants in the development of machine learning systems.