Creative Writers' Attitudes on Writing as Training Data for Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913721080610816 |
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| author | Gero, Katy Ilonka Desai, Meera Schnitzler, Carly Eom, Nayun Cushman, Jack Glassman, Elena L. |
| author_facet | Gero, Katy Ilonka Desai, Meera Schnitzler, Carly Eom, Nayun Cushman, Jack Glassman, Elena L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14281 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Creative Writers' Attitudes on Writing as Training Data for Large Language Models Gero, Katy Ilonka Desai, Meera Schnitzler, Carly Eom, Nayun Cushman, Jack Glassman, Elena L. Human-Computer Interaction 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. |
| title | Creative Writers' Attitudes on Writing as Training Data for Large Language Models |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.14281 |