# Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages

arXiv: https://arxiv.org/abs/2308.13479 · HTML: https://mlsanity.com/paper/2308.13479/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://mlsanity.com/paper/2305.08283/) (2023)
- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports](https://mlsanity.com/paper/2212.14882/) (2022)
- [ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning](https://mlsanity.com/paper/2304.06588/) (2023)
- [Putting GPT-3's Creativity to the (Alternative Uses) Test](https://mlsanity.com/paper/2206.08932/) (2022)
- [Can language models handle recursively nested grammatical structures? A case study on comparing models and humans](https://mlsanity.com/paper/2210.15303/) (2022)
- [ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness](https://mlsanity.com/paper/2305.12947/) (2023)
- [LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs](https://mlsanity.com/paper/2307.10168/) (2023)
- [ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models](https://mlsanity.com/paper/2306.04563/) (2023)
- [Directed Diversity: Leveraging Language Embedding Distances for Collective Creativity in Crowd Ideation](https://mlsanity.com/paper/2101.06030/) (2021)
- [Artificial Intelligence for Health Message Generation: Theory, Method, and an Empirical Study Using Prompt Engineering](https://mlsanity.com/paper/2212.07507/) (2022)
- [Are Language Models Worse than Humans at Following Prompts? It's Complicated](https://mlsanity.com/paper/2301.07085/) (2023)

## Built on by (most cited first, 10 of 10)

- [AI Hallucinations: A Misnomer Worth Clarifying](https://mlsanity.com/paper/2401.06796/) (2024)
- [ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions](https://mlsanity.com/paper/2310.01331/) (2023)
- [Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation](https://mlsanity.com/paper/2401.06643/) (2024)
- [Large Language Models show both individual and collective creativity comparable to humans](https://mlsanity.com/paper/2412.03151/) (2024)
- [SpeechWeave: Diverse Multilingual Synthetic Text & Audio Data Generation Pipeline for Training Text to Speech Models](https://mlsanity.com/paper/2509.14270/) (2025)
- [Cross-cultural Inspiration Detection and Analysis in Real and LLM-generated Social Media Data](https://mlsanity.com/paper/2404.12933/) (2024)
- [Multimodal Fusion with LLMs for Engagement Prediction in Natural Conversation](https://mlsanity.com/paper/2409.09135/) (2024)
- [Working with Large Language Models to Enhance Messaging Effectiveness for Vaccine Confidence](https://mlsanity.com/paper/2504.09857/) (2025)
- [Keeping Users Engaged During Repeated Administration of the Same Questionnaire: Using Large Language Models to Reliably Diversify Questions](https://mlsanity.com/paper/2311.12707/) (2023)
- [Incorporating Different Verbal Cues to Improve Text-Based Computer-Delivered Health Messaging](https://mlsanity.com/paper/2404.13633/) (2024)

## Abstract (arXiv)

Large language models (LLMs) are increasingly capable and prevalent, and can be used to produce creative content. The quality of content is influenced by the prompt used, with more specific prompts that incorporate examples generally producing better results. On from this, it could be seen that using instructions written for crowdsourcing tasks (that are specific and include examples to guide workers) could prove effective LLM prompts. To explore this, we used a previous crowdsourcing pipeline that gave examples to people to help them generate a collectively diverse corpus of motivational messages. We then used this same pipeline to generate messages using GPT-4, and compared the collective diversity of messages from: (1) crowd-writers, (2) GPT-4 using the pipeline, and (3 & 4) two baseline GPT-4 prompts. We found that the LLM prompts using the crowdsourcing pipeline caused GPT-4 to produce more diverse messages than the two baseline prompts. We also discuss implications from messages generated by both human writers and LLMs.