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While LLMs have shown great success in understanding and generating text intraditional conversational settings, their potential for performing ill-definedcomplex tasks is largely under-studied. Indeed, we are yet to conductcomprehensive benchmarking studies with multiple LLMs that are exclusivelyfocused on a complex task. However, conducting such benchmarking studies ischallenging because of the large variations in LLMs' performance when differentprompt types/styles are used and different degrees of detail are provided inthe prompts. To address this issue, the paper proposes a general taxonomy thatcan be used to design prompts with specific properties in order to perform awide range of complex tasks. This taxonomy will allow future benchmarkingstudies to report the specific categories of prompts used as part of the study,enabling meaningful comparisons across different studies. Also, by establishinga common standard through this taxonomy, researchers will be able to draw moreaccurate conclusions about LLMs' performance on a specific complex task.

TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks