Unlike routine tasks where consistency is prized, in creativity and
innovation the goal is to create a diverse set of ideas. This paper delves into
the burgeoning interest in employing Artificial Intelligence (AI) to enhance
the productivity and quality of the idea generation process. While previous
studies have found that the average quality of AI ideas is quite high, prior
research also has pointed to the inability of AI-based brainstorming to create
sufficient dispersion of ideas, which limits novelty and the quality of the
overall best idea. Our research investigates methods to increase the dispersion
in AI-generated ideas. Using GPT-4, we explore the effect of different
prompting methods on Cosine Similarity, the number of unique ideas, and the
speed with which the idea space gets exhausted. We do this in the domain of
developing a new product development for college students, priced under $50. In
this context, we find that (1) pools of ideas generated by GPT-4 with various
plausible prompts are less diverse than ideas generated by groups of human
subjects (2) the diversity of AI generated ideas can be substantially improved
using prompt engineering (3) Chain-of-Thought (CoT) prompting leads to the
highest diversity of ideas of all prompts we evaluated and was able to come
close to what is achieved by groups of human subjects. It also was capable of
generating the highest number of unique ideas of any prompt we studied.

本论文研究了利用人工智能 Enhance 创意生成过程的兴趣，通过使用 GPT-4 探索了不同提示方法对余弦相似性、独特思想数量和思想空间耗尽速度的影响，在大学生开发新产品的领域中发现，由 GPT-4 生成的可行提示的思想池比人类主体生成的思想多样性较小，通过提示工程可以大大提高人工智能生成的思想的多样性，思路链提示对于所有我们评估的提示中的思想多样性最高，并且能够生成任何我们研究过的提示的最大独特思想数量。

多种思想引导：增加人工智能创意差异

Prompting Diverse Ideas: Increasing AI Idea Variance

This study introduces novel methods for sentiment and opinion classification
of tweets to support the New Product Development (NPD) process. Two popular
word embedding techniques, Word2Vec and BERT, were evaluated as inputs for
classic Machine Learning and Deep Learning algorithms to identify the
best-performing approach in sentiment analysis and opinion detection with
limited data. The results revealed that BERT word embeddings combined with
Balanced Random Forest yielded the most accurate single model for both
sentiment analysis and opinion detection on a use case. Additionally, the paper
provides feedback for future product development performing word graph analysis
of the tweets with same sentiment to highlight potential areas of improvement.

该研究介绍了新的方法来支持新产品开发 (NPD) 过程中的情感和意见分类。此研究评估了两种流行的词嵌入技术 Word2Vec 和 BERT，并使用经典机器学习和深度学习算法将它们作为输入来识别情感分析和意见检测的最佳表现方式，并发现 BERT 词嵌入加上平衡随机森林是用例情感分析和意见检测的最准确单一模型。另外，本文还通过对具有相同情感的 tweets 的单词图分析提供未来产品开发的反馈，以突出潜在的改进领域。