In this paper, drawing inspiration from the human creativity literature, we explore the optimal balance between novelty and usefulness in generative Artificial Intelligence (AI) systems. We posit that overemphasizing either aspect can lead to limitations such as hallucinations and memorization. Hallucinations, characterized by AI responses containing random inaccuracies or falsehoods, emerge when models prioritize novelty over usefulness. Memorization, where AI models reproduce content from their training data, results from an excessive focus on usefulness, potentially limiting creativity. To address these challenges, we propose a framework that includes domain-specific analysis, data and transfer learning, user preferences and customization, custom evaluation metrics, and collaboration mechanisms. Our approach aims to generate content that is both novel and useful within specific domains, while considering the unique requirements of various contexts.

本文通过人类创造力文献的启示，探索生成人工智能系统中新奇和实用之间的最佳平衡，提出了包括领域特定分析、数据和迁移学习、用户喜好和定制、自定义评估指标和协作机制在内的框架，旨在在特定领域内生成既新颖又有用的内容。

生成人工智能的创新前沿：管理新颖性和实用性的平衡