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.

通过使用众包任务的具体和包含示例的指导性说明书，我们发现，使用众包管道作为大语言模型（LLMs）的提示可以产生比基线提示更多样化的信息。同时，我们还讨论了人类作者和 LLMs 生成的信息的其他影响。

通过提示大型语言模型生成多样化的励志信息：与人工书写的信息相比较

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

Understanding natural language questions entails the ability to break down a
question into the requisite steps for computing its answer. In this work, we
introduce a Question Decomposition Meaning Representation (QDMR) for questions.
QDMR constitutes the ordered list of steps, expressed through natural language,
that are necessary for answering a question. We develop a crowdsourcing
pipeline, showing that quality QDMRs can be annotated at scale, and release the
Break dataset, containing over 83K pairs of questions and their QDMRs. We
demonstrate the utility of QDMR by showing that (a) it can be used to improve
open-domain question answering on the HotpotQA dataset, (b) it can be
deterministically converted to a pseudo-SQL formal language, which can
alleviate annotation in semantic parsing applications. Last, we use Break to
train a sequence-to-sequence model with copying that parses questions into QDMR
structures, and show that it substantially outperforms several natural
baselines.

本研究提出了问题分解意义表示（QDMR）来解决自然语言问题分解步骤的计算问题，并通过众包管道进行了标注。利用 QDMR 改进了 HotpotQA 数据集的开放领域问题回答，并将其转换成伪 SQL 语言，在语义分析应用中减轻注释难度。通过 Break 训练序列到序列模型，并表明其性能优于几种自然基线的算法。