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.

翻译：大型语言模型（LLMs）的能力日益增强且应用广泛，可用于生成创意内容。内容质量受所用提示词的影响，其中包含示例的更具针对性的提示词通常能产生更优效果。由此可推断，针对众包任务编写的指令（具有针对性且包含示例以指导工作者）或可作为有效的LLM提示词。为探究此可能性，我们采用了以往一项众包流程——该流程向参与者提供示例以生成集体多样化的激励信息语料库。随后，我们使用相同流程让GPT-4生成信息，并比较了以下四类信息的集体多样性：（1）众包撰写者生成的信息；（2）GPT-4按该流程生成的信息；（3和4）两种基线GPT-4提示词生成的信息。研究发现，采用众包流程的LLM提示词促使GPT-4生成的信息比两种基线提示词更具多样性。此外，我们还讨论了人类撰写者与LLMs生成信息的相关启示。