Collecting diverse human data on subjective NLP topics is costly and
challenging. As Large Language Models (LLMs) have developed human-like
capabilities, there is a recent trend in collaborative efforts between humans
and LLMs for generating diverse data, offering potential scalable and efficient
solutions. However, the extent of LLMs' capability to generate diverse
perspectives on subjective topics remains an unexplored question. In this
study, we investigate LLMs' capacity for generating diverse perspectives and
rationales on subjective topics, such as social norms and argumentative texts.
We formulate this problem as diversity extraction in LLMs and propose a
criteria-based prompting technique to ground diverse opinions and measure
perspective diversity from the generated criteria words. Our results show that
measuring semantic diversity through sentence embeddings and distance metrics
is not enough to measure perspective diversity. To see how far we can extract
diverse perspectives from LLMs, or called diversity coverage, we employ a
step-by-step recall prompting for generating more outputs from the model in an
iterative manner. As we apply our prompting method to other tasks (hate speech
labeling and story continuation), indeed we find that LLMs are able to generate
diverse opinions according to the degree of task subjectivity.

调查了大型语言模型（LLMs）在生成多样化观点和理由方面的能力，提出了一种基于标准提示技术来衡量透视多样性的方法，并发现利用句子嵌入和距离度量来衡量语义多样性是不够的。研究结果表明，LLMs 能够根据任务主观性的程度产生多样的观点。