使用大型语言模型进行创新中的创意生成
AI and Its Impact on Creativity and Diversity: An Empirical Study of LLM-Generated Product Ideas
AI总结：
该研究对比人类与GPT-4生成的大学生适用低价新产品创意，发现AI生成创意平均购买意向更高、跻身前10%的概率是人类的7倍，但新颖性较低，少样本提示效果略优于零样本。
AI中文摘要：
本研究评估大型语言模型（LLMs）在生成新产品创意方面的效能。为此，我们针对面向大学生、定价50美元及以下的新产品，比较了三组创意。第一组创意由LLMs问世前修读产品设计课程的大学生创作；第二、三组创意分别由OpenAI的GPT-4通过零样本（zero-shot）和少样本（few-shot）提示生成。我们采用标准市场调研技术评估创意质量，以预测平均购买意向概率；运用文本挖掘评估创意相似度；并通过人工评分者评估创意新颖性。研究发现，AI生成的创意在平均购买意向方面优于人类生成的创意，且少样本提示产生的意向略高于零样本提示。不过，AI生成的创意被认为新颖性较低，成对相似度更高，少样本提示的情况尤为明显，表明解决方案多样性不足。当聚焦最佳创意而非平均创意的质量时，我们发现AI生成的创意跻身前10%的可能性是人类生成的7倍，展现出显著优势。我们认为这7比1的优势是保守估计，因为未考虑AI更高的生产力。研究结果表明，尽管存在一些缺陷，AI创造力在为新产品开发生成高质量创意方面具有重大益处。
英文摘要：
This research examines how well large language models, or LLMs, generate new product ideas for college students priced under $50. Across a series of studies, we identify key strengths and weaknesses of using LLMs for product innovation. Our first study shows that LLM-generated product ideas have higher average quality than human ideas, based on purchase intent, and are 7 times more likely to rank in the top 10%. Our second study shows that this AI-induced creativity boost is not explained by the LLM's more persuasive pitching skills. Our third and fourth studies identify a weakness of using LLMs for brainstorming: AI-generated ideas are less novel at the idea level and less diverse at the set level. In our fifth study, we analyze prior LLM-based creativity studies and find consistently lower idea diversity across all of them, demonstrating the generalizability of these findings. Our sixth and seventh studies investigate techniques to mitigate this diversity loss. We compare LLMs from different vendors and versions and find that more recent models generate more diverse ideas, though they still fall short of human-level diversity. We also demonstrate techniques that increase idea diversity almost to the level of human idea generation: pooling ideas across vendors; prompt engineering, including Chain-of-Thought prompting and injecting heterogeneous personas or constraints; and creative agents that broadly explore the solution landscape to restore diversity. Finally, in our eighth study, we show that exploiting the near-zero marginal cost of AI idea generation by scaling the number of ideas steadily improves coverage of the idea space, approaching human-level coverage. We conclude by presenting actionable recommendations for innovation managers who want to identify better new product ideas with the help of LLMs.
