  While LLMs have shown great success in understanding and generating text in
traditional conversational settings, their potential for performing ill-defined
complex tasks is largely under-studied. Indeed, we are yet to conduct
comprehensive benchmarking studies with multiple LLMs that are exclusively
focused on a complex task. However, conducting such benchmarking studies is
challenging because of the large variations in LLMs' performance when different
prompt types/styles are used and different degrees of detail are provided in
the prompts. To address this issue, the paper proposes a general taxonomy that
can be used to design prompts with specific properties in order to perform a
wide range of complex tasks. This taxonomy will allow future benchmarking
studies to report the specific categories of prompts used as part of the study,
enabling meaningful comparisons across different studies. Also, by establishing
a common standard through this taxonomy, researchers will be able to draw more
accurate conclusions about LLMs' performance on a specific complex task.
