  We are amidst an explosion of artificial intelligence research, particularly
around large language models (LLMs). These models have a range of applications
across domains like medicine, finance, commonsense knowledge graphs, and
crowdsourcing. Investigation into LLMs as part of crowdsourcing workflows
remains an under-explored space. The crowdsourcing research community has
produced a body of work investigating workflows and methods for managing
complex tasks using hybrid human-AI methods. Within crowdsourcing, the role of
LLMs can be envisioned as akin to a cog in a larger wheel of workflows. From an
empirical standpoint, little is currently understood about how LLMs can improve
the effectiveness of crowdsourcing workflows and how such workflows can be
evaluated. In this work, we present a vision for exploring this gap from the
perspectives of various stakeholders involved in the crowdsourcing paradigm --
the task requesters, crowd workers, platforms, and end-users. We identify
junctures in typical crowdsourcing workflows at which the introduction of LLMs
can play a beneficial role and propose means to augment existing design
patterns for crowd work.
