生成式人工智能的创造前沿：管理新颖性与有用性之间的权衡
The Creative Frontier of Generative AI: Managing the Novelty-Usefulness Tradeoff
AI总结：
本文针对生成式AI在新颖性与有用性失衡时产生的幻觉和记忆问题，提出融合领域分析、迁移学习、用户定制、评估指标与协作机制的平衡框架。
摘要：
在本文中，我们从人类创造力文献中汲取灵感，探讨生成式人工智能（AI）系统在新颖性与有用性之间的最佳平衡。我们假设，过度强调其中任何一个方面都可能导致幻觉和记忆等局限。幻觉的特征是AI响应包含随机的不准确或虚假内容，当模型将新颖性置于有用性之上时就会出现。记忆则是指AI模型复现其训练数据中的内容，源于对有用性的过度关注，并可能限制创造力。为应对这些挑战，我们提出一个框架，涵盖特定领域分析、数据与迁移学习、用户偏好与定制化、定制评估指标以及协作机制。我们的方法旨在在特定领域内生成既新颖又有用的内容，同时考虑不同情境的独特要求。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
