Prompting a Large Language Model to Generate Diverse Motivational
  Messages: A Comparison with Human-Written Messages
Samuel Rhys Cox Ashraf Abdul Wei Tsang Ooi
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.