The Creative Frontier of Generative AI: Managing the Novelty-Usefulness Tradeoff

TL;DR

This paper investigates balancing novelty and usefulness in generative AI to enhance creativity while minimizing hallucinations and memorization, proposing a framework for domain-specific content generation.

Contribution

It introduces a novel framework that manages the tradeoff between novelty and usefulness in generative AI, incorporating domain analysis, user preferences, and custom evaluation metrics.

Findings

Framework effectively balances novelty and usefulness

Reduces hallucinations and memorization issues

Enhances domain-specific AI content generation

Abstract

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.