Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4

Chang, Kent; Cramer, Mackenzie; Soni, Sandeep; Bamman, David

doi:10.18653/v1/2023.emnlp-main.453

Cited by 20 publications

References 33 publications

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Literacy in the Time of Artificial Intelligence

Kalantzis,

Cope

2024

Reading Research Quarterly

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The latest mutation of Artificial Intelligence, Generative AI, is more than anything a technology of writing. It is a machine that can write. In a world‐historical frame, the significance of this cannot be understated. This is a technology in which the unnatural language of code tangles with the natural language of everyday life. Its form of writing, moreover, is multimodal, able not only to write text as conventionally understood, but also to “read” images by matching textual labels and to “write” images from textual prompts. Within the scope of this peculiarly mechanical manufacturing of writing are mathematics, actionable software procedure, and algorithm. This paper explores the consequences of Generative AI for literacy teaching and learning. In its first part, we speak theoretically and historically, suggesting that this development is perhaps as momentous for society and education as Pi Sheng's invention of moveable type and Gutenberg's printing press—and in its peculiar ways just as problematic. In the paper's second part, we go on to propose that literacy in the time of AI requires a new way to speak about itself, a revised “grammar” of sorts. In a third part, we discuss an experimental application we have developed that puts Generative AI to work in support of literacy and learning. We end with some findings and implications for literacy education and with a proposal for what we will call cyber‐social literacy learning.

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Literacy in the Time of Artificial Intelligence

Kalantzis,

Cope

2024

Reading Research Quarterly

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Nob-MIAs: Non-biased Membership Inference Attacks Assessment on Large Language Models with Ex-Post Dataset Construction

Eichler,

Champeil,

Anciaux

et al. 2024

Lecture Notes in Computer Science

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On reading and interpreting black box deep neural networks

Dobson

2023

Int J Digit Humanities

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The deep neural networks used in computer vision and in recent large language models are widely recognized as black boxes, a term that describes their complicated architectures and opaque decision-making mechanisms. This essay outlines several different strategies through which humanist researchers and critics of machine learning might better understand and interpret the class of deep learning methods known as Transformers. These strategies expose different aspects of what might be “learned” as Transformers are trained and used in the analysis of language and can help critics at least partially open the black box of machine learning. They are also especially useful for digital humanists using these models as part of a research program informed by tool criticism in which the use of computational tools is conceived of as a metainterpretive act.

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Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4

Cited by 20 publications

References 33 publications

Literacy in the Time of Artificial Intelligence

Literacy in the Time of Artificial Intelligence

Nob-MIAs: Non-biased Membership Inference Attacks Assessment on Large Language Models with Ex-Post Dataset Construction

On reading and interpreting black box deep neural networks

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