# Prompting Diverse Ideas: Increasing AI Idea Variance

arXiv: https://arxiv.org/abs/2402.01727 · HTML: https://mlsanity.com/paper/2402.01727/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 9 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Built on by (most cited first, 14 of 14)

- [Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents](https://mlsanity.com/paper/2410.13185/) (2024)
- [When Search Engine Services meet Large Language Models: Visions and Challenges](https://mlsanity.com/paper/2407.00128/) (2024)
- [MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation](https://mlsanity.com/paper/2406.05690/) (2024)
- [Measuring the Gap Between Human and LLM Research Ideas](https://mlsanity.com/paper/2607.01233/) (2026)
- [SpeechWeave: Diverse Multilingual Synthetic Text & Audio Data Generation Pipeline for Training Text to Speech Models](https://mlsanity.com/paper/2509.14270/) (2025)
- [Cognitive Spillover in Human-AI Teams](https://mlsanity.com/paper/2407.17489/) (2024)
- [Diverse AI Personas Can Mitigate the Homogenization Effect in Human-AI Collaborative Ideation](https://mlsanity.com/paper/2504.13868/) (2025)
- [Reflections and New Directions for Human-Centered Large Language Models](https://mlsanity.com/paper/2605.06901/) (2026)
- [Graph2Idea:Retrieval-Augmented Scientific Idea Generation with Graph-Structured Contexts](https://mlsanity.com/paper/2606.09105/) (2026)
- [EvoGens: A Population-Based Heuristic Search Framework for Scientific Idea Generation](https://mlsanity.com/paper/2605.30961/) (2026)
- [Feeling Guilty Being a c(ai)borg: Navigating the Tensions Between Guilt and Empowerment in AI Use](https://mlsanity.com/paper/2506.00094/) (2025)
- [More Is Not More: What Matters for Diversity in LLM Opinions?](https://mlsanity.com/paper/2607.20429/) (2026)
- [How Strong Is the Evidence for the Artificial Hivemind? Reevaluating Evidence for the Open-Ended Homogeneity of Language Models](https://mlsanity.com/paper/2609.33936/) (2026)
- [Narrowing the Horizon: Quantifying Topic Saliency Shifts in Generative Monoculture](https://mlsanity.com/paper/2609.35302/) (2026)

## Abstract (arXiv)

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.