论文里GPT-4用零样本和少样本生成创意，平均购买意向超大学生，顶尖创意前10%概率是人类的7倍，不过创意相似度高。
研究将大学产品设计课程学生提出的创意与GPT-4在零样本和少样本提示下生成的创意对比，目标是为大学生设计50美元以内新产品。使用市场研究法预测平均购买意向，AI创意得分高于人类创意，其中少样本提示略高于零样本。人类评分显示AI创意新颖性较低，文本挖掘显示AI创意彼此更相似，少样本尤其明显。聚焦最佳创意时，AI创意进入前10%的概率是人类的7倍，且这一优势被视作保守估计。
Using Large Language Models for Idea Generation in Innovation
This research evaluates the efficacy of large language models (LLMs) in generating new product ideas. To do so, we compare three pools of ideas for new products targeted toward college students and priced at 50 dollars or less. The first pool of ideas was created by university students in a product design course before the availability of LLMs. The second and third pools of ideas were generated by GPT-4 from OpenAI using zero-shot and few-shot prompting, respectively. We evaluated idea quality using standard market research techniques to predict average purchase intent probability. We used text mining to assess idea similarity and human raters to evaluate idea novelty. We find that AI-generated ideas outperform human-generated ideas in terms of average purchase intent, with few-shot prompting yielding slightly higher intent than zero-shot prompting. However, AI-generated ideas are perceived as less novel and exhibit higher pairwise similarity, particularly with few-shot prompting, indicating a less diverse solution landscape. When focusing on the quality of the best ideas rather than the average ideas, we find that AI-generated ideas are seven times more likely to rank among the top 10 percent of ideas, demonstrating a significant advantage over human-generated ideas. We propose that this seven-to-one advantage is a conservative estimate because it does not account for the greater productivity of AI. Our findings suggest that despite some drawbacks, AI creativity presents a substantial benefit in generating high-quality ideas for new product development.