We present a novel AI-based ideation assistant and evaluate it in a user study with a group of innovators. The key contribution of our work is twofold: we propose a method of idea exploration in a constrained domain by means of LLM-supported semantic navigation of problem and solution spaces, and employ novel automated data input filtering to improve generations. We found that semantic exploration is preferred to the traditional prompt-output interactions, measured both in explicit survey rankings, and in terms of innovation assistant engagement, where 2.1x more generations were performed using semantic exploration. We also show that filtering input data with metrics such as relevancy, coherence and human alignment leads to improved generations in the same metrics as well as enhanced quality of experience among innovators.

翻译：我们提出了一种新型的基于人工智能的创意生成辅助系统，并通过与一组创新者进行的用户研究对其进行了评估。我们工作的关键贡献体现在两个方面：首先，我们提出了一种在受限领域内进行创意探索的方法，该方法通过大语言模型支持的问题空间与解决方案空间的语义导航来实现；其次，我们采用了新颖的自动化数据输入过滤技术以改进生成内容。研究发现，无论是在明确的调查排名中，还是在创新辅助工具的参与度方面（使用语义探索进行的生成次数是传统提示-输出交互方式的2.1倍），语义探索都比传统的提示-输出交互方式更受青睐。我们还证明，使用相关性、连贯性以及与人类意图对齐度等指标对输入数据进行过滤，不仅能提升生成内容在这些指标上的表现，还能增强创新者的体验质量。