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.

翻译：本文借鉴人类创造力研究的启示，探索生成式人工智能系统在新颖性与实用性之间的最优平衡点。我们认为，过度强调任何一方面都可能导致幻觉和记忆化等局限性。当模型优先考虑新颖性而忽视实用性时，会产生包含随机错误或虚假信息的AI响应，即幻觉现象。记忆化则指AI模型重现训练数据中的内容，这源于对实用性的过度关注，可能限制创造性。为应对这些挑战，我们提出一个包含领域特异性分析、数据与迁移学习、用户偏好与定制化、定制化评估指标及协作机制的综合框架。该方法旨在特定领域内生成兼具新颖性与实用性的内容，同时兼顾不同应用场景的特殊需求。