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Creative Writers' Attitudes on Writing as Training Data for Large Language Models

Authors :
Gero, Katy Ilonka
Desai, Meera
Schnitzler, Carly
Eom, Nayun
Cushman, Jack
Glassman, Elena L.
Publication Year :
2024

Abstract

The use of creative writing as training data for large language models (LLMS) is highly contentious. While some argue that such use constitutes "fair use" and therefore does not require consent or compensation, others argue that consent and compensation is the morally correct approach. In this paper, we seek to understand how creative writers reason about the real or hypothetical use of their writing as training data and under what conditions, if any, they would consent to their writing being used. We interviewed 33 writers with variation across genre, method of publishing, degree of professionalization, and attitudes toward and engagement with LLMs. Through a grounded theory analysis, we report on core principles that writers express and how these principles can be at odds with their realistic expectations for how institutions engage with their work.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2409.14281
Document Type :
Working Paper