Large language models are revolutionizing several areas, including artificial creativity. However, the process of generation in machines profoundly diverges from that observed in humans. In particular, machine generation is characterized by a lack of intentionality and an underlying creative process. We propose a method called Creative Beam Search that uses Diverse Beam Search and LLM-as-a-Judge to perform response generation and response validation. The results of a qualitative experiment show how our approach can provide better output than standard sampling techniques. We also show that the response validation step is a necessary complement to the response generation step.

翻译：大语言模型正在多个领域引发革命，包括人工智能创造力领域。然而，机器的生成过程与人类观察到的过程存在根本性差异。具体而言，机器生成的特点是缺乏意图性和潜在的创造性过程。我们提出一种名为创造性束搜索（Creative Beam Search）的方法，该方法利用多样束搜索和"大语言模型即评判者"（LLM-as-a-Judge）机制，执行响应生成与响应验证。定性实验结果表明，相较于标准采样技术，我们的方法能够提供更优输出。同时，我们证明响应验证步骤是响应生成步骤的必要补充。