The Creative Frontier of Generative AI: Managing the Novelty-Usefulness Tradeoff
It addresses a domain-specific problem for AI developers and users, offering incremental improvements in managing generative AI outputs.
The paper tackles the tradeoff between novelty and usefulness in generative AI systems, proposing a framework to balance them and reduce issues like hallucinations and memorization.
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.