Copyright Infringement Risk Reduction via Chain-of-Thought and Task Instruction Prompting
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866918252430491648 |
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| author | Sarna, Neeraj Li, Yuanyuan von Gablenz, Michael |
| author_facet | Sarna, Neeraj Li, Yuanyuan von Gablenz, Michael |
| contents | Large scale text-to-image generation models can memorize and reproduce their training dataset. Since the training dataset often contains copyrighted material, reproduction of training dataset poses a copyright infringement risk, which could result in legal liabilities and financial losses for both the AI user and the developer. The current works explores the potential of chain-of-thought and task instruction prompting in reducing copyrighted content generation. To this end, we present a formulation that combines these two techniques with two other copyright mitigation strategies: a) negative prompting, and b) prompt re-writing. We study the generated images in terms their similarity to a copyrighted image and their relevance of the user input. We present numerical experiments on a variety of models and provide insights on the effectiveness of the aforementioned techniques for varying model complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15442 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Copyright Infringement Risk Reduction via Chain-of-Thought and Task Instruction Prompting Sarna, Neeraj Li, Yuanyuan von Gablenz, Michael Machine Learning Large scale text-to-image generation models can memorize and reproduce their training dataset. Since the training dataset often contains copyrighted material, reproduction of training dataset poses a copyright infringement risk, which could result in legal liabilities and financial losses for both the AI user and the developer. The current works explores the potential of chain-of-thought and task instruction prompting in reducing copyrighted content generation. To this end, we present a formulation that combines these two techniques with two other copyright mitigation strategies: a) negative prompting, and b) prompt re-writing. We study the generated images in terms their similarity to a copyrighted image and their relevance of the user input. We present numerical experiments on a variety of models and provide insights on the effectiveness of the aforementioned techniques for varying model complexity. |
| title | Copyright Infringement Risk Reduction via Chain-of-Thought and Task Instruction Prompting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.15442 |