Copyright Infringement Risk Reduction via Chain-of-Thought and Task Instruction Prompting

• URL: http://arxiv.org/abs/2512.15442v1
• Date: Wed, 17 Dec 2025 13:39:17 GMT
• Title: Copyright Infringement Risk Reduction via Chain-of-Thought and Task Instruction Prompting
• Authors: Neeraj Sarna, Yuanyuan Li, Michael von Gablenz,
• Abstract summary: reproduction of training dataset poses a copyright infringement risk.<n>We present a formulation that combines chain-of-thought and task instruction prompting in reducing copyrighted content generation.<n>We study the generated images in terms their similarity to a copyrighted image and their relevance to the user input.
• Score: 7.04790342345686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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