Large Language Models (LLMs) are increasingly used for boosting
organizational efficiency and automating tasks. While not originally designed
for complex cognitive processes, recent efforts have further extended to employ
LLMs in activities such as reasoning, planning, and decision-making. In
business processes, such abilities could be invaluable for leveraging on the
massive corpora LLMs have been trained on for gaining deep understanding of
such processes. In this work, we plant the seeds for the development of a
benchmark to assess the ability of LLMs to reason about causal and process
perspectives of business operations. We refer to this view as
Causally-augmented Business Processes (BP^C). The core of the benchmark
comprises a set of BP^C related situations, a set of questions about these
situations, and a set of deductive rules employed to systematically resolve the
ground truth answers to these questions. Also with the power of LLMs, the seed
is then instantiated into a larger-scale set of domain-specific situations and
questions. Reasoning on BP^C is of crucial importance for process interventions
and process improvement. Our benchmark could be used in one of two possible
modalities: testing the performance of any target LLM and training an LLM to
advance its capability to reason about BP^C.

利用大型语言模型 (LLMs) 来推动组织效率和自动化任务，在本研究中创建了一个基准来评估 LLMs 在推理业务活动的因果和过程视角方面的能力，并应用 LLMs 的能力进行 BP^C 的推理，以用于流程干预和流程改进。