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

要旨

The use of creative writing as training data for large language models (LLMs) is highly contentious and many writers have expressed outrage at the use of their work without consent or compensation. In this paper, we seek to understand how creative writers reason about the real or hypothetical use of their writing as training data. We interviewed 33 writers with variation across genre, method of publishing, degree of professionalization, and attitudes toward and engagement with LLMs. We report on core principles that writers express (support of the creative chain, respect for writers and writing, and the human element of creativity) and how these principles can be at odds with their realistic expectations of the world (a lack of control, industry-scale impacts, and interpretation of scale). Collectively these findings demonstrate that writers have a nuanced understanding of LLMs and are more concerned with power imbalances than the technology itself.

受賞
Best Paper
著者
Katy Ilonka. Gero
Harvard University, Cambridge, Massachusetts, United States
Meera Desai
University of Michigan, Ann Arbor, Michigan, United States
Carly Schnitzler
Johns Hopkins University, Baltimore, Maryland, United States
Nayun Eom
Harvard University, Cambridge, Massachusetts, United States
Jack Cushman
Harvard University, Cambridge, Massachusetts, United States
Elena L.. Glassman
Harvard University, Allston, Massachusetts, United States
DOI

10.1145/3706598.3713287

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713287

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Using AI or Not

G314+G315
7 件の発表
2025-04-29 01:20:00
2025-04-29 02:50:00
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